Jacob King Final Year Project

of 59
BEng(Hons) Digital Broadcast Technology
School of Computing, Science and Engineering
Newton Building, University of Salford, England
This paper represents my own work. Any input or work done by other people is clearly noted and properly referenced.
A Comparative Study of Next Generation
Video Compression Techniques
Jacob King
@00292474
j.king@edu.salford.ac.uk
Supervisor: Dr Francis Li
Reader: Dr Bill Davies

of 59
Abstract
HEVC and VP9 are the most recent developments in video compression technology
aimed at addressing the problem of storing and transmitting UHDTV in an efficient and
commercially viable manner. This paper looks at the techniques they employ in their
encoders and conducts subjective testing to investigate which codec is likely to become
the most dominant.

of 59
Acknowledgements
This research was supported by assistance from Dr Francis Li, Laurence Murphy, Dawn
Shaw, and Dr Marianne Patera; all lecturers at the University of Salford, who provided
insight, expertise, and equipment that helped make this paper possible.

King Next Generation Video Compression
Page 4 of 59
Table
of
Contents

Abstract
...............................................................
2

Acknowledgements
.........................................
3

1.
Introduction
.............................................
6

2.
How
the
eye
works
................................
7

2.1.
The
Basics
of
Light
...............................
7

2.2.
Biology
of
the
eye
.................................
7

2.2.1.
Rods
and
Cones
.....................................
8

2.2.2.
Sight
Impairments
...............................
8

2.3.
Perception
of
Motion
Pictures
.........
9

3.
Early
compression
standards
............
9

3.1.
ITU-‐T
H.261
............................................
9

3.2.
Further
developments
......................
10

4.
MPEG
Development
............................
10

4.1.
MPEG-‐1
...................................................
10

4.1.1.
Group
of
Pictures
(GOP)
.................
10

4.1.2.
Macroblocks
........................................
11

4.2.
MPEG-‐2
(H.262)
...................................
11

4.2.1.
Profiles
and
Levels
...........................
11

4.3.
MPEG-‐4
Part
10
(H.264/AVC)
.........
13

4.3.1.
Slices
.......................................................
13

4.3.2.
Intra
Coding
.........................................
14

4.3.3.
Inter
Coding
.........................................
14

4.3.4.
Transformation
..................................
15

4.3.5.
Entropy
Coding
..................................
15

4.3.6.
Profiles
Overview
.............................
17

5.
HEVC
(High
efficiency
Video
Coding)

18

5.1.
Quadtree
Coding
Structure
.............
18

5.2.
Parallelisation
.....................................
19

5.2.1.
Slices
.......................................................
19

5.2.2.
Tiles
.........................................................
20

5.2.3.
Wavefront
Parallel
Processing

(WPP)
21

5.3.
Intra
Picture
Coding
..........................
21

5.4.
Entropy
Coding
....................................
22

5.5.
Inter
Picture
Coding
..........................
22

5.6.
Profiles
...................................................
22

5.7.
Other
Features
.....................................
23

6.
VP9
...........................................................
23

6.1.
Improvements
Upon
VP8
.................
23

6.2.
Coding
Structure
................................
23

6.3.
Intra
Prediction
..................................
24

6.4.
Inter
Prediction
..................................
25

6.4.1.
GOP
Structure
and
Alternate

Reference
Frames
..............................................
25

6.4.2.
Motion
Vectors
...................................
25

6.5.
Entropy
Coding
...................................
25

6.6.
Transformation
..................................
26

6.7.
Parallelisation
.....................................
26

6.7.1.
Tiling
.......................................................
26

6.7.2.
Frame-‐Level
Parallelism
................
26

6.8.
Segmentation
......................................
26

6.9.
Profiles
..................................................
27

7.
Subjective
Testing
Methodology
and

Evaluation
......................................................
27

7.1.
Equipment
............................................
27

7.1.1.
Encoding
Hardware
.........................
27

7.1.2.
Television
Monitor
...........................
28

7.1.3.
Decoding
Hardware
.........................
28

7.1.4.
Testing
Space
......................................
28

7.2.
Testing
Procedure
.............................
29

7.3.
Test
Material
.......................................
30

8.
Encoder
configurations
.....................
32

8.1.
Shared
Settings
...................................
32

8.2.
VP9
Configuration
..............................
32

8.3.
HEVC
Configuration
...........................
33

9.
Results
and
Statistical
analysis
.......
33

9.1.
Differences
Between
Participant

Groups
...............................................................
34

9.2.
Differences
Between
Bitrates
........
36

9.3.
Differences
Between
Content
........
36

9.4.
The
Interaction
Between
Content

and
Bitrate
.......................................................
37

9.4.1.
Sintel
.......................................................
37

9.4.2.
Crowd
Run
...........................................
38

9.4.3.
Cobra
......................................................
39

9.4.4.
Clown
.....................................................
40

9.5.
Participant
Comments
.....................
41

9.6.
Summary
...............................................
41

10.
Conclusion
...........................................
42

11.
Further
Work
.....................................
43

12.
REFERENCES
.......................................
44

Page 5 of 59
Appendix
A:
Table
of
defined
MPEG-‐2

Profiles
and
Levels
.........................................
47

Appendix
B:
Table
of
maximum
number
of

enhancement
layers
for
each
MPEG-‐2

scalable
profile
................................................
47

Appendix
C:
The
different
directional

modes
of
Intra
4x4
coding
...........................
48

Appendix
D:
HEVC
picture
partitioning

compared
with
H.264
picture
partitioning

..............................................................................
49

Appendix
E:
Subjective
Testing
Encoder

Commands
........................................................
49

Appendix
F:
Boxplots
of
results
.................
50

Appendix
G:
Test
Participants
Comments

..............................................................................
53

Glossary
of
Important
Terms
and

Equations
..........................................................
54

Page 6 of 59
1. INTRODUCTION
Since the realisation that it is possible to store
and transmit video digitally there has been a
requirement to do so in the most efficient
manner possible. With each new evolution of
video technology come new challenges that
need to be addressed by the video compression
codecs that ultimately make them a viable
product to market. This has followed motion
picture evolution starting with CIF right the
way through to HDTV, and, most recently,
Ultra High Definition (UHD) 4K and 8K video
technologies. Ultra High Definition not only
presents the problem of increasing resolution,
but also increased frame rate and a Higher
Dynamic Range (HDR) colour spectrum;
ultimately presenting the challenge of
efficiently storing and transmitting data at
extortionate bitrates never before experienced
by the commercial broadcast industry.
Until now, compression standards have been
efficient enough to transmit SD and HD digital
video over the available electromagnetic
spectrum, but UHD standards compressed
using these standards would still use more than
8 times the bandwidth of HD.
Cue HEVC and VP9: two exciting new,
cutting edge codecs from vastly different
backgrounds, designed to tackle UHD storage
and transmission head on. On one hand is
HEVC; a codec with a rich family of
successful video compression codecs behind it,
designed by the Motion Picture Experts Group
(MPEG). On the other hand is VP9; an open-
source codec designed around online video
streaming and developed by Google, a relative
newcomer to the video compression industry.
The goal of both codecs is to provide video
compression that can be decoded on consumer
level hardware without a significant increase
in cost, and achieve 50% more efficiency than
has been seen in any codecs before them.
HEVC is built upon the popular H.264 (AVC)
codec previously developed by MPEG, and
used for the transmission of HDTV in Europe.
HEVC takes many of the techniques used in
AVC and improves their efficiency at the cost
of increased complexity at the encoder.
Similarly, VP9 makes advances on Google’s
previous codec VP8, which gained momentum
as a natively decodable codec for web
browsers, and is popularly known as the
WEBM project. VP9s main market is internet
streaming, and is intended to be implemented
into Youtube (owned by Google) as the
primary codec for HD and UHD video.
This paper looks at the techniques used in
both codecs to encode video, and compares
their results in subjective tests to determine
where each codec excels and ascertain which
codec is most likely to be more dominant.

Page 7 of 59
2. HOW THE EYE WORKS
In order to understand the way in which we
can compare the visual aspects of different
video compression methods, it is necessary to
know how the eye functions and interprets
moving images.
Sight, as defined by L. A. Remington (2012),
occurs when “the visual system takes in
information from the environment in the form
of light and analyzes and interprets it”.
This chapter will briefly explore the way in
which the human eye interprets light, and, in
particular, moving images.
2.1. The Basics of Light
The human eye sees light in three different
planes: Luminance (brightness), hue, and
saturation (fig.2.1). These planes are crucial to
understanding way that codecs encode visual
information.
Luminance is the intensity of light per area of
light measured, and is more commonly
referred to as brightness outside of the media
industry. The eye is more sensitive to
luminance than hue or saturation. This
information, therefore, dominates encoded
video files and streams.
Hue is the wavelength of the light and is the
primary way that humans determine the
dominant colour being reflected from an
objects surface.
Saturation is the measure of the mixture
between the dominant hue and white light
wavelengths. An unsaturated image will be
seen as “paler” than one that is more saturated.
Figure 2.1: Visual diagram of hue, saturation, and
luminance (brightness)
2.2. Biology of the eye
The globe of the eye is made up of 3 layers:
the Sclera, the Cornea, and the Retina (fig. 2.2)
(Davson, 1980).
Light enters the eye through the cornea and is
initially refracted by its surface, then by the
lens, and finally, forms an image on the retina.
This information is then transported to the
brain along the optical nerve containing about
a million neurons (Cornsweet, 1970; Davson,
1980).

Page 8 of 59
Figure 2.2: Biology of the eye
2.2.1. Rods and Cones
The rods and cones are photoreceptive cells
contained in the retina and form the system
that interprets hue, saturation, and luminance.
The rod cells interpret luminance only, and the
cones interpret colour information. The cones
differ in the wavelengths they perceive and are
categorized as being red, green, and blue
(RGB) photoreceptors (Cornsweet, 1970).
Humans are, therefore, a trichromatic species.
This is the reason that colour video is designed
to reproduce three colours that can be
combined to display almost any colour
perceivable in the visible spectrum.
2.2.2. Sight Impairments
As the human eye is so complex, small genetic
mutations can cause issues to arise with the
way that it processes information. One of the
most common such issues is monochromacy,
or colour blindness. This is a condition where
the retina only contains the rod photoreceptors;
therefore, it is unable to distinguish between
different colours.
A similar condition, dichromacy, is diagnosed
when the retina only contains two of the three
photoreceptors needed to perceive the full
colour spectrum. A dichromat cannot correctly
identify certain portions of the visible light
spectrum, depending on which type of
photoreceptor their retina is missing
(Cornsweet, 1970).
These defects are identifiable using the
Ishihara test for colour blindness, constructed
in 1917 by Dr. Shinobu Ishihara (Ishihara,
1917), shown in figure 2.3.
It is important to identify these defects to
ensure that any subjective tests take into
account the presence of any monochromatic or
dichromatic subjects.
Figure 2.3: An example test plate from the Ishihara
Colour Blindness test. A subject with fully functioning,
trichromatic vision will see the number 5; a
monochromat would not be able to distinguish between
the different coloured dots (Source: www.colour-
blindness.com).

Page 9 of 59
2.3. Perception of Motion Pictures
Motion pictures are created using a series of
still images shown in quick succession. This
creates the illusion of constant motion and is
the basis of all cinematography.
Two of the main traits that are useful in
creating this illusion are the persistence of
vision and the critical flicker-fusion threshold
(CFF).
The persistence of vision is how long an image
is retained on the retina after the light source
has been removed. This varies depending on
the angle the source is approaching the retina
from, but from an optimal viewing angle,
images are retained on the retina for 40-60mS
(Hardy, 1919).
In association with this is the critical flicker-
fusion threshold (CFF), which is the point at
which a light source alternating between bright
flashes and no light, is perceived as a constant
light without any flickering. It is determined
by the relation between the intensity of the
light source, and the frequency of
intermittence, in cycles per minute. This
means that higher luminance increases the
CFF, meaning that for brighter light sources,
more cycles per minute are required to
perceive a constant source (Landis, 1954).
3. EARLY COMPRESSION
STANDARDS
A need for digital video compression was
realized when it became apparent that
technological advancements, such as the
compact disk and the internet, would create a
medium for digital media to be stored and
transmitted more freely. This prompted the
International Telecommunications Union
(ITU, formerly CCITT) to develop a standard
to allow for such methods of storage and
transmission to be transparent worldwide; the
first, most practical, of which was the ITU-T
recommendation H.261.
A lot of the early techniques used for video
compression were based on those used for
image compression, such as JPEG.
3.1. ITU-T H.261
ITU-T Recommendation H.261 was designed
between 1988 and 1989. The recommendation
describes “the video coding and decoding
methods for the moving picture component of
audiovisual services at the rates of p x 64
Kbps, where p is in the range 1 to 30”. The
goal of the recommendation was to develop a
video encoding method that would enable
efficient video conferencing across ADSL
networks (International Telecommunications
Union, 1988).
The codec uses inter-frame prediction to
remove temporal redundancy, and the Discrete
Cosine Transform (For a description and
example of the DCT see Glossary) to remove
spatial redundancy.
Other, optional, features of the codec include:
motion compensation, and Forward Error
Correction (FEC) to enable the decoder to
make an informed estimation of any missing
data.
One key aspect of the recommendation is that
it specifies that it will only work for
progressive motion pictures.
The development of the H.261 codec paved
the way for future codec development, and is

Page 10 of 59
the first in the H.26x series of codecs that are
explored later in this document.
3.2. Further developments
H.261 lead to the development of two other
key, consumer level, codecs from the 1990s:
Cinepak and Indeo.
Cinepak, developed by Supermac, was one of
the first popular codecs used on PCs. After its
release in 1991, it was incorporated into
Apple’s Quicktime in 1992, and then into
Windows in 1993 (Segaretro.org, 2014).
It used very similar compression methods to
H.261, but fell out of favour to MPEG-2.
Indeo was developed in 1992 by Intel for the
emerging video conferencing industry. It
encoded YUV video in an asymmetrical way,
which meant that encoding the video was more
time consuming than decoding it. In addition
to this, Indeo was a scalable codec, meaning
that less powerful computers could decode the
video at lower frame rates or frame sizes, than
more powerful machines (Delargy, 1996).
These features made the codec very attractive
for the low powered computers of the 1990s,
but it too fell out of favour to the rise of
MPEG.
4. MPEG DEVELOPMENT
The Moving Picture Experts Group (MPEG)
was formed in 1988 by the International
Standards Organisation (ISO) to address the
need for a common video compression
standard. MPEG took the approach of
standardising the way that a decoder would
interpret a bit stream, as opposed to the way
that an encoder would create one (Watkinson,
2008).
4.1. MPEG-1
Their first standard, MPEG-1 (ISO 11172),
was released in 1992. It used similar encoding
methods to those used in the JPEG, and was
largely an extension of the H.261 codec
(Ghanbari, 1999), with the goal of encoding
video at the same rate as conventional CDs
(1.5Mb/s) following the same principal of
asymmetry as in Indeo.
This standard formed the basis for all
subsequent MPEG standards, introducing
features such as elementary stream syntax, bi-
directional motion compensation (B frames), a
Group of Pictures (GOP), buffering,
macroblocks, and rate control; however it did
not support interlaced or HD video and was
therefore unsuitable for digital television
broadcasting (Watkinson, 2004).
4.1.1. Group of Pictures (GOP)
In MPEG1, and subsequent standards, there
are three types of frame that are used to
construct a video sequence: Intra (I) frames,
Predictive (P) frames and Bi-directional (B)
frames (Figure 4.1). These frames form what
is known as a Group of Pictures (GOP), where
the start of each GOP is indicated by an I
frame.
Intra frames have the lowest compression ratio
as they only use intra-coding to remove spatial
redundancy. Therefore, I frames don’t
reference other frames, but are used as a
reference by P and B frames.
Predictive frames use information from the
previous P or I frame to predict the
information in the present frame. This reduces
both spatial and temporal redundancy.

Page 11 of 59
Bi-directional frames predict the picture
information from both future and past P or I
frames1
. B frames have the highest
compression ratio of the three frames but as
such are also the most inaccurate, therefore,
there are usually no more than two B frames
between successive P or I frames (Benoit,
2002).
Figure 4.1: The MPEG GOP structure
(www.provideocoalition.com)
4.1.2. Macroblocks
Macroblocks are formed from four 8x8 pixel
blocks, to make one 16x16, 4:2:0 sampled
(MPEG-1), macroblock. The DCT processing
is performed independently within the blocks
(BSI, 1993).
Macroblocks include motion vector
information that is used by P and B frames for
motion estimation and compensation. Bi-
directional frames use motion vector
information from both past and future I- or P-
frames to predict the current position of the
macroblock.
1
This means that a buffer is required in the bit
stream in order to re-order the GOP so that the
decoder can use the information of the future P
or I frame that the B frame is referencing;
which adds latency to the signal path.
4.2. MPEG-2 (H.262)
The MPEG-2 standard, ratified in 1995 by the
ITU (ITU-T Rec. H.262), is an extended and
improved version of MPEG-1, with interlacing
capabilities and support for a much larger
range of bit rates and resolutions. As such,
MPEG-2 decoders are also capable of
decoding MPEG-1 video.
The main improvement upon MPEG-1 is the
inclusion of different profiles and levels.
Watkinson (2004, P.22) states “put simply a
profile describes a degree of complexity,
whereas a level describes the picture size or
resolution that goes with that profile”. This
includes a profile that is able to encode
1920x1080 HD video.
Interlaced video can be encoded either as
separate fields or as separate frames, and can
be adjusted to either format on a frame-by-
frame basis (International
Telecommunications Union, 1995).
The limited chrominance information that was
available in MPEG-1 4:2:0 sampling was
improved upon with the additional capability
to sample at 4:2:2 and 4:4:4.
4.2.1. Profiles and Levels
The different profiles and levels in MPEG-2
form a hierarchical structure whereby each
profile and level, is an improvement upon all
lower profiles and levels. The six profiles are:
Simple, Main, SNR scalable, Spatial scalable,
4:2:2 (only defined for use with the Main
level) and High. The four possible levels are:
Low, Main, High 1440, and High (For an
overview of the defined profiles and levels, see
Appendix A).

Page 12 of 59
The main profile, at main level, is used in
Europe by DVB for standard definition
television (Digital Video Broadcasting, 2014).
The simple profile doesn’t support B frames
and is only defined at main level, it is therefore
relatively easy for less powerful hardware to
encode and decode. The lack of B frames also
means that there will be less latency in the
signal chain.
Scalable Profiles
Three of the defined profiles have the
capability to transmit a scalable signal that can
be decoded at two levels of quality. This is
designed to provide resilience in the signal
chain (the base signal is sent with a higher
priority), and also reduce the bandwidth
needed to transmit two signals of different
quality e.g. an SD and HD signal.
The SNR (Signal to Noise Ratio) profile
creates scalable signals by transmitting a base,
“noisy” signal, and a noise-cancelling,
enhancement signal.
The base signal contains the information for a
low quality picture, which can then be
combined with the noise-cancelling signal to
produce a higher quality picture. This is done
by encoding the base signal; decoding the
resulting signal locally, in order to compare
the output to the input, and calculate the
quantization error; which is transmitted as the
noise-cancelling, enhancement signal (Figure
4.2). A low complexity decoder will only
decode the base signal, but a higher
complexity decoder combines the base and
enhancement signals to produce a higher
quality picture (Ghanbari, 2011).
Figure 4.2: SNR encoder overview
The Spatial profile uses up to three signals
carrying video at different resolutions and/or
sample rates.
Downscaling a high quality encoded signal
generates a base signal. The base signal is then
decoded locally and up-scaled to match the
high quality resolution. The decoded signal is
then subtracted from the high quality signal
and the difference is encoded as the
enhancement layer (Figure 4.3) (Ghanbari,
2011). The spatial profile also allows for an
additional SNR enhancement layer to further
improve video quality.
Figure 4.3: Spatial encoder overview
The High profile can combine these techniques
to have up to two enhancement layers. It also
allows for 4:2:2 video to be encoded, whereas
the SNR and spatial profiles can only encode
with 4:2:0 sampling.

Page 13 of 59
The maximum number of possible
enhancement layers for each scalable profile
can be seen in Appendix B.
4.3. MPEG-4 Part 10 (H.264/AVC)
In 2001; recognising that the cost of
processing power and storage had reduced;
and that network capabilities had been
improved enormously since the development
of H.262; the ITU Video Coding Expert Group
(VCEG), and the ISO Moving Picture Expert
Group (MPEG), joined together to form the
Joint Visual Team (JVT) and begin
development of the H.264 codec; the first
edition of which was approved in 2003 (ITU,
2013).
The resulting standard, sometimes referred to
as Advanced Video Coding (AVC), is an
improved and extended version of the MPEG-
2 standard, and, since its conception, has
replaced many codecs as the default choice in
a wide range of applications; from video
telephony to HD video broadcasting.
The standard was developed with the same
philosophy of an asymmetrical signal chain
and, in the case of H.264, the resulting encoder
is typically eight times more complex than an
MPEG-2 encoder (Ibrahim, 2007).
H.264 introduces a lot of new features to the
MPEG family of codecs (ITU, 2013),
including:
• Improved error resilience
• Low delay mode for
telecommunications
• Slices
• A de-blocking filter
• B frame referencing
• Multiple-frame referencing for P- and
B-frames
• DCT replaced by a transform with an
exact inverse transform
• Two new types of entropy coding to
replace Variable Length Coding
• New and improved methods of intra
and inter coding
4.3.1. Slices
One of the major differences between H.264
and earlier standards is the new way of looking
at a picture in terms of slices instead of whole
frames. This can be done in three different
ways:
• By looking at a frame as one slice
• By dividing the frame into slices with
equal numbers of macroblocks;
resulting in varying packet sizes for
each slice
• By dividing the frame into slices with
equal packet size; resulting in varying
numbers of macroblocks per slice
It also means that instead of I-, P-, and B-
frames, I-, P-, and B-slices are generated; with
the addition of two new slice types: SP
(Switching Predictive) and SI (Switching
Intra) slices.
All slices representing a picture do not need to
be of the same type, but in practice this is most
commonly the case; therefore in most
circumstances, unless stated otherwise, it is

Page 14 of 59
still acceptable to refer to frames as I-, P-, and
B-frames (Ghanbari, 2011).
SP and SI Slices
These new types of slice replace I-frames in a
video stream as a point where switching to a
stream of a different bit-rate or resolution is
possible. As they use inter frame prediction
they use less bandwidth than I frames and are
therefore more efficient. They also have
applications in splicing, random access, fast
forwarding, rewinding, and error recovery.
In error recovery, an SP frame is sent to the
decoder, referencing a frame that was correctly
decoded, to create a point where the decoder
can re-synchronise with the encoder
(Ghanbari, 2011).
4.3.2. Intra Coding
Spatial prediction in H.264 references decoded
pixel blocks above and to the left of the
current block to make a reasonable estimation
of the luminance value of each pixel within the
current block. This is based upon the idea that
adjacent macroblocks tend to have similar
textures. However, since adjacent blocks may
be from P- or B-slices, data from these blocks
is not used for the spatial prediction process to
avoid error propagation in the signal chain.
There are three main types of intra-coding for
the luminance signal that are used in H.264,
with varying levels of accuracy dependent
upon the complexity of the picture being
encoded.
Intra 4x4
Intra 4x4 is used for the most detailed areas of
an image, and encodes at block level. This
method spatially predicts pixel values in 8
different directional modes, or an average
(DC) mode, from the neighbouring blocks.
This reduces prediction error as pixels are
predicted in the same orientation as the
texture, or in DC mode if the block comprises
of a single luminance value. For a table of the
different Intra 4x4 modes, see Appendix C.
A similar intra coding method, Intra 8x8, uses
the same technique.
Intra 16x16
Intra 16x16 treats the macroblock as a whole
and only has four modes, which operate in a
similar way to the equivalent Intra 4x4 modes.
These are horizontal, vertical, average (DC),
and plane. In plane mode, pixels are predicted
with reference to the pixels both adjacent, and
above the macroblock being encoded.
Intra 16x16 is much more efficient for areas
where there is less detail as it uses much less
data than Intra 4x4.
Chrominance information is encoded using the
same techniques as Intra 16x16 but it operates
on 8x8 blocks of chrominance.
I_PCM
In I_PCM mode, raw PCM data is recorded
without prediction of transformation in order
to retain all of the macroblock information.
This is used for very high quality encoding.
4.3.3. Inter Coding
Inter coding in H.264 introduces 3 main
features that were not present in previous
standards (Ghanbari, 2011):

Page 15 of 59
• Variable block sizes for motion
estimation
• Quarter-pixel precision for motion
vectors
• Multiple referencing for P- and B-
frames
These new features mean that inter frame
prediction is more complex and more accurate
than inter frame prediction used in MPEG-2.
Variable Block Sizes
The 16x16 macroblocks that were used for
motion estimation in MPEG-2 are inaccurate if
the moving object that is being encoded is
smaller than the size of the macroblock or
crosses, but does not completely fill, multiple
macroblocks. This is most noticeable at lower
resolutions (Watkinson, 2004).
In H.264, blocks can be encoded at multiple
sizes, ranging from 4x4 pixels to 16x16. This
also includes rectangular blocks in sizes such
as 16x8 and 4x8.
In doing this the motion estimation is more
accurate than MPEG-2, however, smaller
block sizes means a larger volume of data
overhead.
Quarter-Pixel Precision for Motion Vectors
MPEG-2 could encode motion vectors with
half-pixel precision, whereas H.264 can
encode luminance sample motion vectors with
quarter-pixel precision, and chrominance
sample motion vectors with up to one-eighths
pixel precision. This obviously results in much
more accurate motion estimation.
Multiple-Frame Referencing for P- and B-
Frames
In MPEG-1 and MPEG-2, P-frames were only
able to reference one frame, and B-frames
could reference a maximum of two frames.
H.264 allows multiple-frame referencing
(known as weighted prediction) of up to 16
frames for both P- and B-frames meaning that
inter frame prediction results in much better
quality. This also means that different
macroblocks within the same picture can be
predicted using blocks from multiple different
frames. This reduces the amount of data that
needs to encoded, and improves accuracy, as
only the smallest difference between
macroblocks is encoded (Ghanbari, 2011).
In addition to this, H.264 allows B-frames to
be used as references, although this feature is
rarely exploited due to the inevitable
inaccuracies that it would present in
comparison to I- and P-frame referencing.
4.3.4. Transformation
In the same way that previous standards
remove the redundancy from the encoded pixel
values in intra and inter coding using the
Discrete Cosine Transform, H.264 uses a more
accurate variation of the DCT known as the
Integer Transform. This uses a 4x4 variation
on the 8x8 DCT which has an exact inverse
transform, eliminating all transformation
mismatches that were present in the DCT
method, therefore allowing lossless
compression.
4.3.5. Entropy Coding
Prior to the release of H.264 most codecs used
Variable Length Coding to encode the entropy
in a lossless format. H.264 introduces two new

Page 16 of 59
types of entropy coding: Context-Adaptive
Variable Length Coding (CAVLC), and
Context-Adaptive Binary Arithmetic Coding
(CABAC).
Before either CAVLC or CABAC are applied
to the data, it is zig-zag scanned across the
block, from the most frequent value to the
least frequent to produce one string containing
all values.
CAVLC
In CAVLC, typical properties of the transform
coefficients (results of the transform), found
after quantization and transformation, are
exploited to code the entropy more efficiently
(Heo, Kim, and Ho, 2010).
These properties are:
• Transform coefficients typically
contain high numbers of zeroes;
particularly in high frequency areas
• Most nonzero coefficients are
sequences of ±1s (Trailing 1s) with
equal probability
• The volume of nonzero coefficients
tends to be higher towards the low
frequency regions of the transform
• Nonzero values in adjacent blocks tend
to be highly correlated so they can use
the same look-up tables
Taking advantage of these properties,
CAVLC then applies the following steps:
1. The nonzero coefficients and
trailing 1s are encoded with a
combined codeword
2. The sign of each trailing 1 is
encoded using a one bit codeword
3. The absolute value (ignoring the
sign) of each nonzero coefficient is
encoded using the look up tables,
and their signs are encoded using a
one bit codeword
4. The number of all zeroes before the
last nonzero coefficient is encoded
5. The number of zeroes preceding
each nonzero coefficient is encoded
CABAC
CABAC is based on arithmetic coding which
has been proven to produce much better levels
of compression than Variable Length Coding;
however, it is also much more computationally
expensive (Seabrook, 1989).
CAVLC requires at least one bit to represent
each symbol, which is inefficient for symbols
with a probability less than 0.5 (Ghanbari,
2011). In arithmetic coding, blocks of symbols
are assigned a single code word, meaning that
it can achieve an average of less than one bit
per symbol. This is done by subdividing values
into blocks between 0 and 1, according to the
probability of each symbol in the word, until a
value is found that can represent the whole
word. The resulting value is then binarised
(converted so that it can be represented using
binary) (Mathematicalmonk, 2011).

Page 17 of 59
In addition to this, arithmetic coding separates
the statistical modelling from the coding so
any statistical model can be used along side it.
This means that context adaptation is very
flexible but to limit the number of models that
are used in one bit stream, and therefore
limiting the amount of extra data needed to be
transmitted to decode the stream, H.264 only
uses four different types of statistical analysis.
4.3.6. Profiles Overview
Similar to previous MPEG standards, H.264
has different profiles designed to be used for
different applications, with varying complexity
and compression ratios.
Baseline Profile
The baseline profile is the least complex
profile, and therefore has the lowest
compression ratio. It was designed for real
time applications, such as video telephony and
video conferencing.
This profile only uses I- and P-frames, uses the
simpler CAVLC entropy coding, and large
amounts of built in error resiliency to cope
with hostile networks.
Main Profile
This profile uses I-, P-, and B-frames, and can
select between using CAVLC and CABAC
entropy coding. It also introduces the
capability to encode interlaced video.
The main profile has the highest possible
compression ratio but does not include any
error-resilience tools as its use is designed for
video storage (High-Definition DVDs) and
transmission on ‘clean’ networks.
Another key feature of the main profile is the
use of weighted prediction.
Extended Profile
This profile includes all of the features of the
baseline profile, with the added features of
weighted prediction and B-frames. However,
this profile does not support interlacing or
CABAC entropy coding.
The unique feature of this profile is the
addition of SP- and SI-frames for switching
between video streams. Therefore, the best
application for this profile is online video
streaming.
High Profiles
The high profiles are an extension of the main
profile with the additional capability of
adaptive block sizes for intra coding.
There are 4 high profiles that use the features
above but different bit and sampling rates:
• High – 8 bit, 4:2:0
• High 10 – 10 bit, 4:2:0
• High 4:2:2 – 10 bit, 4:2:2
• High 4:4:4 – 12 bit, 4:4:4
An overview of the key features of each
profile can be seen in figure 4.4.

Page 18 of 59
Figure 4.4: An overview of the H.264 profiles key
features (Richardson, 2003)
5. HEVC (HIGH EFFICIENCY VIDEO
CODING)
Work on HEVC (ITU Rec. H.265) began in
2010 by the Joint Visual Team (JVT) who had
previously developed the Advanced Video
Coding (MPEG 4 part 10, ITU Rec. H.264)
standard.
HEVC was developed to address the need for
better video coding efficiency of HD and post-
HD (4K, 8K) video. The main focus of the
standard is on increased video resolution and
the use of parallel processing architectures.
The first version of the standard was released
in January 2013, followed by the release of the
second version in October 2014.
The standard introduces several new features
that have not been present in previous MPEG
or ITU coding standards, including:
• A new Quadtree based coding structure
that replaces the macroblock structure
• Advanced Motion Vector Prediction
• Quarter-sample Motion Compensation
accuracy
• 35 intra picture prediction modes
(compared with 9 in H.264)
• Improved CABAC entropy coding
• Enhanced parallelisation features and
the introduction of Tiles and
Wavefront Parallel Processing
• 2-Byte Network Abstraction Layer
(NAL) packet headers that identify the
packet more efficiently
• Compatibility with ITU rec. 2020 for
Higher Dynamic Range colour
5.1. Quadtree Coding Structure
The Quadtree coding structure used in HEVC
introduces improved flexibility and variety in
the way that the codec segments a picture into
blocks of pixels. The quadtree coding
structure allows the codec to split each unit
down into quarters until the necessary pixel
block size is reached. This is done to
efficiently encode each section of the picture
according to the amount of detail contained
within it. Appendix D demonstrates how
HEVC partitions a picture using fewer blocks
compared with H.264.
Each unit in the Quadtree structure is
comprised of equivalent luminance and
chrominance blocks.

Page 19 of 59
In previous standards a macroblock with a
fixed size of 16x16 luminance samples was
used. The equivalent unit in HEVC, known as
the Coding Tree Unit (CTU), can be
comprised of 16x16, 32x32, or 64x64 pixels
meaning that greater compression ratios can be
achieved (Sullivan et al, 2012). The CTU size
is defined at the start of a video sequence and
remains the same throughout.
The CTUs can then be further broken down
into Coding Units (CUs). The CU can range
from 8x8 pixels in size, to the same size as the
CTU it is contained within, and each CU can
be broken down into smaller CUs
independently. The CU defines an area using
the same prediction mode (intra or inter).
The CU is then further split into Prediction
Units (PUs), which are used to store motion
vector or intra-picture prediction information
(depending on the prediction mode of the CU).
A PB can be MxN in size (rectangular) or
MxM (square), and is equal to, or smaller than,
the size of the CU (Figure 5.1).
Figure 5.1: Prediction Block Sizes
The CU is also split into Transform Units
(TUs), which define blocks where individual
transforms take place within the CU. A TU
can be formed across the CU as a whole, or on
individual luminance and chrominance Coding
Blocks (CBs).
Performing a transform on the whole CU may
produce inaccurate results if the CU contains
both detailed and plain parts, therefore it is
possible to partition a CU into smaller TUs
(Figure 5.2). TUs and PUs do not have to
correlate, and it is more often the case that it is
less efficient to do so.
Figure 5.2: Transform Block Sizes
5.2. Parallelisation
Parallelism, as defined by Misra et al (2012),
“reduces the need for increased clock speeds
on single-core processors that represent an
increase in cost and heat production. By
subdividing a picture you can allocate different
parts of it to be processed by different cores in
the encoding decoding architecture”. By taking
advantage of the parallelisation options offered
by HEVC, encoder and decoder hardware is
able to process video in a more efficient way
than was possible with H.264, where slices
offered limited parallelisation.
HEVC offers three parallelisation options:
slices, tiles, and Wavefront Parallel Processing
(WPP).
5.2.1. Slices
Slices in HEVC work in the same way as in
H.264 (see 4.3.1), but to reduce the amount of
data overhead that they introduce at low bit
rates, HEVC also offers dependent slices. With
dependent slices, only the first slice segment
contains the slice header information, meaning
that the rest of the slices within the picture
cannot be decoded until the first slice has.
Additionally, dependencies are not broken
along slice borders. This presents an increase

Page 20 of 59
in efficiency, but also increases the possibility
of considerable errors to occur due to packet
loss or corruption in the first slice. Dependent
slices can be further combined with tiles and
WPP for even greater efficiency that will
allow for a large reduction in latency for real-
time applications.
As with H.264 “Slice partitioning can be
defined by the MTU [Maximum Transmission
Unit] of the network or pixel processing
constraints such as the amount of CTBs that
should be contained in each slice.” (Misra et
al, 2013, P.970).
5.2.2. Tiles
Tiles are independently coded, rectangular
regions of a picture formed along the
intersection of CTU rows and columns. They
are processed in raster scan order (left to right,
moving down the picture), as are the CTUs
contained within them.
Tiles share header information to improve the
coding efficiency, and the location of tiles
within a picture is described in a packet header
that contains the locations of the CTU row and
column intersections.
As entropy coding and reconstruction is
independent on each tile, parallelisation is
achieved by processing tiles on separate cores
simultaneously. This also reduces the amount
of buffering required on each core, as the
buffer only needs to store motion vector and
intra-picture coding information for the tiles it
is processing, and not the whole bitstream.
One key advantage of tiles is Region of
Interest (ROI) signalling. Tiles can that are
signalled as ROI contain the most important
region of a picture that needs to be reproduced
the most accurately. When a tile is identified
as ROI, the most capable core is selected to
process it, ensuring the best possible
reproduction.
Slices and tiles can be used together but must
fulfil either of the two criteria below:
• All blocks within a tile must also be
within the same slice (Fig 5.3a)
• All blocks within a slice must also be
within the same tile (Fig 5.3b)
Figure 5.3: (a) Tiles contained within a single slice. (b)
Slices contained in separate tiles (Misra et al, 2013).
It is anticipated that the use of tiles will be
particularly prominent in video-conferencing
applications where the load can be spread
across multiple cores to ensure maximum

Page 21 of 59
efficiency and minimum latency; particularly
in mobile devices where multiple cores are
becoming more commonplace (Misra et al,
2013).
5.2.3. Wavefront Parallel Processing
(WPP)
WPP separates a slice into rows of CTUs. The
first row is processed in an ordinary way, the
second row can begin to be processed after
two CTUs have been processed in the first
row, the third row can begin to be processed
after two CTUs have been processed in the
second row, etcetera (Fig. 5.4). The entropy
coding models can be derived from those used
in the previous row, as dependencies are not
removed at the row boundaries (unlike slices
and tiles). This means that WPP could
potentially provide better compression
performance, and avoid introducing artefacts
that could occur along slice and tile boundaries
(Zhang, Zhang, and Gao, 2014).
Figure 5.4: WPP processes rows of CTBs in parallel,
each row starting with the CABAC probabilities
available after processing the second CTB of the row
above (Chi et al, 2012).
Parallelisation using WPP is achieved by
processing each row on a separate core.
However, this does introduce some
inefficiency as not all rows can be processed
simultaneously, and the next picture can only
begin decoding once the first has completed.
This inefficiency could potentially be
overcome by using Overlapped Wavefront
(OWF), where inactive cores can begin
processing the next picture before the current
one has been completed (Chi et al, 2012).
However, at the time of writing, OWF is not
included in the specification for HEVC.
WPP cannot be used with tiles, but can be used
in collaboration with slices.
A comparison of the advantages of the
different parallelisation approaches can be
seen in table 5.1.
Table 5.1: A comparison of parallelisation approaches
in HEVC (Chi et al, 2012)
5.3. Intra Picture Coding
HEVC offers thirty-five different directional
modes (Fig 5.5), compared with only nine in
H.264. In addition to this, the decoded
boundary pixels from neighbouring blocks are
used as references to help determine the
prediction mode of the current block. This
represents improved accuracy, but also an
increase in computational power needed.

Page 22 of 59
Figure 5.5: Spatial prediction directional modes in
HEVC
5.4. Entropy Coding
HEVC only offers CABAC (the more
powerful of the two methods used in H.264) as
its entropy coding method. There are fewer
contexts available to be used than in H.264,
but other improvements to the CABAC
algorithm result in better compression overall
(Sullivan et al, 2012).
5.5. Inter Picture Coding
HEVC uses the same GOP structure as seen in
previous standards, where I-, P-, and B-Slices
are the basis for video coding. Therefore, the
same inter prediction methods are also present,
with some improvements.
One of the main improvements is the inclusion
of Advanced Motion Vector Prediction
(AMVP), which uses information from
adjacent PBs and reference pictures to make
an informed decision on the most likely
motion vector.
Further improvements can also be made using
the merge mode offered by AMVP. This
allows the decoder to predict the motion vector
using information from temporally or spatially
neighbouring picture blocks, improving the
overall accuracy of the process.
5.6. Profiles
There are only three primary profiles that have
been specified for HEVC: Main, Main 10, and
Main Still Picture. In addition to these there
are a range of extension profiles that provide
extra functionality such as scalability and
multi-view video coding. The limited number
of primary profiles is intended to improve
interoperability between devices and improve
transparency between traditionally separated
services such as broadcasting and online
streaming. The profiles specified in HEVC are,
for the most part, improved versions of those
specified in AVC.
Within each profile are various levels that
further define the encoded picture. This
includes limiting the picture size, bit rate, and
sample rate of the bit stream.
The primary profiles are arranged in a
hierarchical structure such that each profile
offers improvements upon its predecessor. The
Main Still Picture profile offers the fewest
tools, followed by the Main profile, which is
then succeeded by the Main 10 profile, which
offers the most advanced compression of the
three primary profiles. Each profile is only
specified to use YCbCr 4:2:0 sampling, but
some extension profiles allow for 4:2:2 and
4:4:4 picture sampling.
Two of the key extension profiles that provide
additional quality sought after at a professional
level, are the Main 12, and Main 4:2:2 12
profiles. The Main 12 profile is an extension
of the Main profile, offering a bit depth of up
to 12 bits. The Main 4:2:2 12 profile is a
further extension of this, but with the addition
of 4:2:2 video sampling.

Page 23 of 59
5.7. Other Features
One of the most notable features of HEVC is
the lack of support for interlaced video. It was
decided that the decreasing distribution of
interlaced video and increasingly obsolete
production of interlaced monitors justified the
use of progressive-only scanning; a decision
which will undoubtedly influence the way that
video is distributed in the future.
The way that the codec handles motion
compensation has also been improved, with
HEVC offering quarter-sample precision (as
introduced in AVC), combined with weighted
prediction and allowance for multiple
reference pictures. This ultimately gives
HEVC superior motion compensation
accuracy compared with previous standards.
6. VP9
Development of VP9 by Google began in 2011
as part of the continuation of the Webm
project, which aimed to introduce an open-
source video standard to the Internet (The
Webm Project, n.d.). Up until Webm was
released in 2010, using VP9’s predecessor
VP8, there was no freely implementable video
format designed for HTML. The goal of VP9
was to produce the same quality output for
50% of the bitrate used in VP8 and H.264.
Support for VP9 is currently natively available
in various web browsers including Google
Chrome, Mozilla Firefox, and Opera. This
widespread support has allowed Google to
introduce VP9 encoding to Youtube, which
they claim has enabled 25% more of their
videos to be viewed in HD, and meant that
they load, on average, 15% faster
(Ramamoorthy, 2014).
Unfortunately, Google have not yet released a
full specification for the codec, but the
information in this chapter is accurate at the
time of writing.
6.1. Improvements Upon VP8
The Webm Project introduces several key
features in VP9 that make significant advances
on VP8 and make it a viable option for many
more applications than just those requiring an
open-source video format for the Internet.
This includes:
• A Variable Bit-rate option
• A Constant Quality (regardless of bit-
rate) option
• A Constrained Quality option that
behaves like a capped VBR option
• 4:2:2 and 4:4:4 colour profiles
(currently experimental)
• 10 and 12 bit video support
• Only progressive encoding (no option
for interlaced video)
• Compatibility with ITU rec. 601, 709,
and 2020 colour spaces, the latter of
which is required for 4K video
6.2. Coding Structure
VP9 uses a similar quad-tree style coding
structure to that used in HEVC, with block
sizes ranging from a Super-block (SB) of
64x64 to the smallest block of 4x4, processed
in raster scan order. One of the key differences
however is that at each block level there are
three potential end-point options, and one
option for further breakdown into four smaller

Page 24 of 59
blocks of equal size (Figure 6.1). The three
end-point options are:
• Treating the parent block as a single
square block
• Splitting the block into two vertically
adjacent, rectangular prediction blocks
• Splitting the block into two
horizontally adjacent, rectangular
prediction blocks
Figure 6.1: Coding structure and processing order in
VP9
In the same way as HEVC, blocks are
separately split into prediction and transform
blocks. However, VP9 offers only the four
possible shapes described above for prediction
blocks, compared with eight available in
HEVC.
6.3. Intra Prediction
VP9 offers only ten intra-prediction modes
compared with thirty-five in HEVC (Figure
6.2). These are:
• DC Prediction
• True-motion Prediction; where each
pixel is predicted using the border
pixels from previously encoded blocks,
both vertically and horizontally
• Horizontal Prediction
• Vertical Prediction
• Six directional modes at 27, 153, 135,
117, 63, and 45 degrees
Figure 6.2: VP9 Intra Prediction Modes
The intra-coding process operates on the
transform blocks within each intra-coded
block. As with HEVC, each transform block is
processed in raster scan order, with each block
going through prediction, coding, and
reconstruction before moving onto the next
one (Mukherjee et al, 2013).

Page 25 of 59
6.4. Inter Prediction
VP9 offers some interesting new techniques
for inter-frame prediction that offer different
advantages to those seen in HEVC.
6.4.1. GOP Structure and Alternate
Reference Frames
Before discussing the ins and outs of VP9s
inter prediction, it is important to understand
it’s GOP structure.
Like most recent lossy codecs before it, VP9
employs both intra-only encoded frames, and
uni-directional predicted frames (predicting
from past frames only). However, HEVC has
the additional advantage of bi-directionally
predicted B-frames, inherited from H.264. Due
to patent restrictions VP9 cannot use bi-
directional frames unless they are not
displayed. This is where an interesting feature
called Alternate Reference Frames (ARFs) is
introduced.
ARFs are used only for prediction and are
never displayed on the screen. This allows for
bi-directional prediction, which is particularly
useful for predicting motion vectors using the
average of a past and future frame (VP9 calls
this “Compound prediction”) (Grange &
Alvestrand, 2013).
As the container for VP9 is designed to display
a every frame that is decoded, ARFs present a
bit of a problem. To avoid causing too much
disruption in the bit stream, ARFs are encoded
into a “super-frame” which contains the data
for multiple ARFs, with one displayed frame.
This means that a picture is displayed for
every chunk of data received at the decoder,
whilst the ARFs are stored in a buffer.
All predicted frames are able to reference up to
three separate frames, including an ARF,
which are selectable from a buffer of eight
frames maintained by the decoder.
Additionally, VP9 allows inter-predicted
frames to be of a different resolution than the
previous frame. This allows for on-the-fly
scaling and bit rate adjustments, which is very
attractive to video conferencing and video
streaming services as it is a much more elegant
solution than other methods such as H.264’s
SVC profile (Kapsenberg, 2013).
6.4.2. Motion Vectors
VP9 has four separate inter-prediction modes
that can be used at each block size:
• NearestMV

• NearMV

• ZeroMV

• NewMV

NearestMV and NearMV both survey motion
vectors of blocks within the same region as the
current block and select the most appropriate
candidate (in a similar way to AMVP in
HEVC); ZeroMV implies that no motion
vector is used; and NewMV encodes a new
motion vector into the stream (Mukherjee et
al, 2013). VP9 has a maximum motion vector
accuracy of 1/8th
of a pixel.
6.5. Entropy Coding
VP9 continues to use the BoolCoder entropy
coder from VP8. This is a binary arithmetic
coding method similar to CABAC from H.264,
but with improved context adaptation in the
VP9 implementation (Grange & Alvestrand,
2013).

Page 26 of 59
6.6. Transformation
VP9 supports three type of transformation:
The DCT as used in H.264; the Asymmetric
Discrete Sine Transform (ADST), which is
suggested to be more efficient than the DCT
for some intra prediction (Grange &
Alvestrand, 2013); and The Walsh-Hadamard
Transform (WHT).
The DCT is used on all inter-coded blocks,
and can be used on all blocks up to 32x32. For
intra-coded blocks, a hybrid of the ADST and
a 1-dimensional DCT can be used. The WHT
is only used at the 4x4 level to losslessly
encode intra pictures.
6.7. Parallelisation
As with HEVC, VP9 has also been designed to
take advantage of increasingly common multi-
core processor architectures. There are two
methods of parallelisation available: frame-
level parallelism and tiling.
6.7.1. Tiling
VP9 uses a similar tiling scheme to that used
in HEVC, with a few subtle differences. In
VP9, tiles are independently coded sub-units
of a frame, but the dependencies for each tile
are broken along column borders only and the
tiles are spaced as evenly as possible; with the
number of tiles in a frame always equalling 2n
(Kapsenberg, 2013). This means that a frame
containing eight tiles (4x4) can only be
decoded using four threads.
6.7.2. Frame-Level Parallelism
When enabled, this mode allows the decoder
to decode the entropy for successive frames in
a semi-parallel manner, providing that required
information from past reference frames has
already been decoded. Frames are then
reconstructed sequentially as they are required
to be displayed (Grange & Alvestrand, 2013).
6.8. Segmentation
Segmentation is an interesting feature that
allows select areas of a frame to have certain
attributes processed differently to the rest of
the frame. Segments are not restricted to a
certain shape, allowing flexibility in their
usage (Kapsenberg, 2013; Grange &
Alvestrand, 2013).
The frame is divided into eight segments, each
of which can have any of four features below
enabled:
Skip
This feature marks the segment as having no
temporal changes in successive frames, i.e. a
static background.
Alternate Quantizer
This feature is useful for marking an area that
needs more (or less) detail than other
segments, and changes the number of
quantization levels to reflect that.
Ref
This feature enables a segment to use a
different reference frame to those indicated in
the frame header.
AltLf
This feature allows the segment to use a
different strength of smoothing filter to the rest
of the frame, which is useful for smoothing out
particularly blocky areas of the picture.

Page 27 of 59
6.9. Profiles
Unfortunately, as there is no official spec for
VP9, there is also no listing of the full features
enabled in each profile.
However, information from the Webm Project
developers (Wilkins, 2013; Ramamoorthy,
2014) suggests that there are four profiles with
the following capabilities:
• Profile
0

o 4:2:0
sampling

• Profile
1

o 4:2:2
and
4:4:4
sampling

• Profile
2

o Same
as
0
but
with
10
or
12

bit
encoding

• Profile
3

o Same
as
1
but
with
10
or
12

bit
encoding

Unfortunately, there is no indication about
which features will be available in each profile
at the time of writing.
7. SUBJECTIVE TESTING
METHODOLOGY AND
EVALUATION
Having looked at the techniques that both
codecs use, and developed an understanding of
how they compare in theory, the next obvious
step is to look at their video outputs.
In this section a description of the testing
methodology and justification for the choice of
encoding parameters is presented.
The chosen test methodology for conducting
the subjective testing is the Double Stimulus
Constant Quality Scale (DSCQS) method,
described by ITU-R BT.500 (2012). The
subjective tests compare video encoded at the
same bit rates by VP9 and HEVC in the UHD1
(3840x2160) resolution.
This method suggests the use of a variety of
test materials, using a set presentation
structure, with allowances for a mixture of
both expert and non-expert participants.
7.1. Equipment
The equipment required for this method of
testing is as follows:
• Encoding hardware
• A television monitor
• A decoder/playback device
• A room with the capability to be set in
ideal test conditions
7.1.1. Encoding Hardware
The encoding hardware used to encode the test
materials was a Macbook Pro. Ideally, a
dedicated server with higher computational
power would be used but the practicality of
transferring large files and the financial cost
implications of a server with sufficient storage
made this impractical. The added benefit of
using a laptop to encode is that it gives
additional insight into the real-world usage of
the codecs using consumer level hardware.
The specifications for the laptop are shown in
table 7.1.

Page 28 of 59
Processor Type Intel Core i5
Processor
Speed
2.4 Ghz (With turbo
boost up to 2.93 Ghz,
and hyper-threading)
RAM 8Gb (2x 1066Mhz
DDR3 4Gb)
Available
Storage
70Gb HDD
Table 7.1: Encoding hardware specification
7.1.2. Television Monitor
As the test material to be used was in the
UHD1 resolution, a 4K capable television
monitor was required.
The available 4K monitor was the 58-inch
LED Panasonic TX-58AX802. This monitor
has USB3 ports and a 4K-capable HDMI input
which provided two potential methods for
displaying the test material. The full television
specification is available from Panasonic (n.d).
Originally, it was planned to play the test
materials directly from a USB3 hard-disk drive
but this would have meant re-encoding the test
material to another codec that the decoder in
the television was able to interpret.
Subsequently, this meant re-encoding to a
lossless format, in order to avoid any
concatenation errors from multiple encodes,
thus significantly increasing the bit rate,
consequently meaning that the television
would not be able to decode the material fast
enough to allow smooth playback.
Therefore, it was decided that the most
appropriate method would be to use external
hardware connected using the 4K HDMI port
on the television.
Before any testing began the levels on the
monitor were checked using EBU colour bars
and test signals.
7.1.3. Decoding Hardware
The decoding hardware used was an MSI
WS60 20J 4K Edition gaming laptop (Table
7.2). This is an extremely high specification
laptop and had the appropriate hardware for
decoding the test material as efficiently as
possible, with the added benefit of an external
4K capable HDMI port.
The HDMI cable used to connect the laptop to
the television was an IBRA High Speed Pro
Gold Red HDMI v2.0/1.4, 4-meter length
cable. The length selected was to enable the
decoding laptop to be out of the peripheral
vision of the test participants.
Processor
4th Generation Intel
Core i7
RAM
16Gb (2x 1600Mhz
DDR3L
Storage Internal 256Gb SSD
Video Output
HDMI 1.4 (4K
capable)
USB USB 3.0
Table 7.2: Relevant specification information of the
MSI WS60 20J 4K Edition Laptop used for decoding
7.1.4. Testing Space
The room used for testing was the Media
Technology laboratory at Salford Universities
Media City campus.
This room has no windows, making it easy to
limit external light sources, and has adequate
space for participants and testing equipment.

Page 29 of 59
This made the room ideal for setting the
required testing conditions.
7.2. Testing Procedure
The test participants were seated within the
176° viewing angle of the screen at a distance
of 1.2 Metres (ideal test conditions for the
monitor being used), with no other light
sources in the room (Figure 7.1).
Figure 7.1: Participant sat in test conditions
The monitor was set to the default contrast and
brightness settings recommended by the
manufacturer.
Once seated, participants were asked to fill in
a short survey asking them:
• Their
age

• Whether
they
identify
as
an
expert

or
non-‐expert
in
the
field
of
video

quality
assessment

• Whether
they
have
any
untreated

visual
impairments
that
would

prevent
them
from
taking
part
in
the

testing

• Their
average
daily
video

consumption

• The
participants
were
then
informed

of
the
testing
procedure
and
asked
if

they
had
any
queries
to
ensure
full

coherence
of
the
testing
procedure.

Before any assessed testing began, a short
series of training videos (direct anchoring) was
presented, using the same structure as the
graded testing, to allow the participants to
adjust to the grading scale and avoid any
discrepancies in their assessment during the
graded test sessions.
The testing procedure, as suggested by ITU-R
BT.500, is as follows: A reference video,
ideally the uncompressed source material, is
presented, followed by a five-second clip of
middle-grey, and then the video that is to be
assessed. This is then followed by another
five-second middle-grey clip, and then the
process is repeated. Participants are asked to
grade the quality of the assessment video in
comparison to the reference during the second
cycle, at the end of which a ten-second
middle-grey clip is shown before beginning
the cycle of the next assessment video (Figure
7.2). The same video sequence is not shown
consecutively.
Figure 7.2: The presentation structure of the test
material as suggested by ITU-R BT.500 (2012)

Page 30 of 59
However, uncompressed video requires
extremely high bitrates for playback, and
therefore requires substantial amounts of
computational power. During practice tests, it
was found that the bit rate of uncompressed
video was too high for the hardware being
used to play the video smoothly. Therefore,
the reference video that was used during
testing was the same video encoded using the
opposite codec to that which was being
assessed.
To avoid any bias towards either codec, the
test materials were presented in a random
order, where the reference video used was
equally either VP9 or HEVC.
This also meant that the grading scale
suggested by the ITU was adjusted to suit the
presentation method. The test material was
therefore graded as either equally as good,
better, or worse than the reference video
(Figure 7.3).
Figure 7.3: Sample of the grading scale used for the
assessment of the test material
Ideally, the test material would be assessed
twice with the reference being HEVC for one
assessment, and VP9 for the other. However,
time constraints of the participants only
allowed for one assessment of the test
material. Overall, both HEVC and VP9 were
used as the reference video an equal number of
times to avoid any bias.
At the end of the testing, the participants were
asked to comment on the reasons behind up to
three of their most extreme grades, and were
offered the option to re-watch those sequences
as a reminder. This was done to gain some
insight into the qualities of the compressed
clips that people found most distracting, and
therefore find which qualities of each codec
viewers found most annoying.
7.3. Test Material
The selection of the testing material is crucial
for generating reliable results. For this test,
four different videos, with differing content
were used. Following the ITU guidelines, each
video was ten seconds long and contained
appropriately critical content. None of the
videos contained any audio in order to avoid
external influences on the participants’
perceptions.
All of the videos were downloaded as either
uncompressed video sources or uncompressed
frame-by-frame PNG images that were
subsequently encoded into a video sequence
using the lossless Apple Quicktime RLE
codec.
The videos used were as follows:
• Sintel
• Crowd Run
• Cobra
• Clown
The “Sintel” clip is taken from the animated
movie “Sintel”, created by the Blender

Page 31 of 59
Foundation using the open source animation
software Blender. The clip contains fast
moving action with lots of delicate detail
around the characters faces and clothing, but
with easy background material (Figure 7.4).
This footage is considered the least difficult to
encode of the four videos.
Figure 7.4: Frame from the Sintel test material
The “Crowd Run” clip is a piece of test
footage used by the Visual Quality Experts
Group (VQEG) subsidiary of the ITU to
exploit weaknesses within codecs (Figure 7.5).
The footage shows the start of a race with a
large crowd running towards the camera. In
addition to this, the background of the footage
contains a tree and an observing crowd with
significant detail that will further test the
capabilities of an encoder. This footage is
considered to be the most difficult to encode
out of the four videos.
Figure 7.5: Frame from the “Crowd Run” test material
The “Cobra” clip is a piece of test footage
provided by Harmonic Inc under a Creative
Commons license (Figure 7.6). The footage
shows a cobra observing its surroundings.
There is a reasonable amount of detail on the
cobra itself, but the background and
surrounding area does not contain a significant
amount of detail.
Figure 7.6: Frame from the “Cobra” test material
Harmonic Inc also provides the “Clown” clip
under a Creative Commons license (Figure
7.7). This footage contains various slow
moving objects, accompanied by a clown
slowly moving his head upwards against a
static background.
Figure 7.7: Frame from the “Clown” test material
The variance in the content of the selection of
videos represents four main categories of
video content: animation (Sintel), sport
(Crowd run), nature (Cobra), and interview

Page 32 of 59
(the moving head against a static background
in the Clown clip).
Each clip was encoded at 2-, 3-, 5-, and 10-
Mbps. These bit-rates were chosen as errors in
compression are more noticeable at lower bit-
rates, so increments of 1-Mbps at higher bit-
rates are unlikely to present any significant
differences.
8. ENCODER CONFIGURATIONS
When it comes to comparing two different
video codecs, intrinsic differences in their
features and algorithms mean that making a
fair and unbiased comparison is extremely
difficult. This study has attempted to conduct a
fair test based on test configurations of
previous studies and recommended settings
from the creators of the codecs.
This chapter details the configurations of both
encoders and the justifications behind the
choice of settings.
8.1. Shared Settings
The same progressive uncompressed, or
losslessly encoded, footage was used by both
encoders as the source for all of the
compressed outputs.
Additionally both encoders were set to
compress each video using a 2-pass encode2
as, at the time of writing, single pass encoding
in VP9 is still in a developmental stage, thus it
would be unfair to use it for a comparison.
2
A 2-pass encode encodes the video once,
stores relevant information in a log file, which
is then used in the second pass to maximise the
quality of the output. 2-pass encoding cannot
be used for live applications.
Both encoders were also set to use medium
speed encoding settings. Ideally, both codecs
would be compared using their best (slowest)
settings but due to the enormous amount of
time taken to do this, in addition to a 2-pass
encode, this was impractical. When the
encoding speeds were compared, a medium
speed encode for both codecs was
approximately 3-4 times faster than a slow
speed encode.
For both codecs the bit rate was controlled
using an average bit-rate (ABR) setting to
ensure equal file sizes for the outputs of both
codecs. Using a constant bit-rate (CBR)
removes any advantages of using 2-pass
encoding3
.
The colour sampling rate was YUV 4:2:0 as
4:2:2 encoding is not currently available in the
encoder being used for VP9.
8.2. VP9 Configuration
To encode the VP9 videos, the “libvpx” library
was used in the Ffmpeg command line
encoder. This encoder was chosen as it’s open-
source and widely recognised as one of the
most efficient implementations available at the
time of writing.
The settings used to encode VP9 were widely
researched and reflected those used in similar
tests by Mukherjee et al (2013), Rerabek &
Ebrahimi (2014), and Grois et al (2013). As
well as the suggested parameters from Google
(The Webm Project, retrieved April 2015).
3
During the first pass the encoder is more
conservative in its approach to allow enough
bit-rate to encode frames further along in the
stream. The second pass can then analyse the
log file of the first pass and allocate more or
less bit-rate to different frames as appropriate.

Page 33 of 59
Tiles and frame parallelisation were enabled to
speed up the encoding process and allow for
smooth decoding. However, it is noted that
Google suggest that turning these off could
offer a small bump in video quality (The
Webm Project, Retrieved April 2015).
Additionally, the Alternate Reference Frame
feature was used and was set to be created
using seven frames with the
“arnr_max_frames” parameter, with a strength
of 5, as recommended by Google.
The encoder was set to allow a reference frame
from up to 25 frames ahead, and the GOP size
was set to allow a GOP of anywhere between
25 and 250 frames to give the encoder
flexibility.
The full command used to encode the VP9
videos can be seen in Appendix E. For a
description of each parameter, please see The
Webm Project (Retrieved April 2015).
8.3. HEVC Configuration
To encode the HEVC videos, the “libx265”
library was used, in the same command line
encoder as VP9, Ffmpeg. Again, at the time of
writing this is considered to be one of the most
efficient implementations of the codec.
The settings for HEVC were also researched
from similar studies by Grois et al (2013) and
Rerabek & Ebrahimi (2014), in addition to the
recommended settings from the Ffmpeg
website (Ffmpeg, retrieved April 2015).
The majority of the recommended settings
were already implemented in the “medium”
preset for x265, however, based on
information from the aforementioned sources,
some parameters were adjusted slightly.
The maximum number of reference frames
that could be used for motion vector prediction
was set to 4 and the maximum number of B-
frames that could be used in one GOP was set
to 16.
Additionally, the “b-adapt” parameter was set
to 2 to allow the encoder to make simultaneous
decisions for multiple B-frames about where
they should be positioned in the GOP, thus
taking advantage of the parallelisation features
available in HEVC.
The command used to encode the HEVC
videos can be seen in Appendix E, and a full
list of the medium preset values can be found
via X265 (retrieved April 2015).
9. RESULTS AND STATISTICAL
ANALYSIS
This section analyses the results obtained from
the subjective testing comparing the video
quality of HEVC and VP9 4K video.
For all data in this section, the scores were
read from the scale using the scale in figure
8.1. For example a score of 0 indicates that the
participant felt that the video quality of the
second video was equal to the first, and a score
of 1.7 indicates that the participant felt the
second video was better than the first etc.

Page 34 of 59
Figure 8.1: The scale from which participants scores
were evaluated
9.1. Differences Between Participant
Groups
In total, 16 participants attended the subjective
testing session; with almost equal amounts
identifying as expert (44%) and non-expert
(56%) (table 9.1). None of the participants
reported visual impairments, and 94% were
aged between 20 and 30 years old.
Table 9.1: Subjective Testing participant information
The overall mean participant score indicates a
neutral grade that tends towards a slight
preference for HEVC. It is interesting to note,
however, that the mean score from the expert
group tends more towards a neutral preference
with much less deviation in scores, and the
non-expert groups’ mean score tends more
towards a slight preference for HEVC.
Similarly, it should be noted that there is less
deviation in the expert groups’ scores,
indicating more consistency (Table 9.2).
These scores are also representative of the
individual participants overall mean score
frequency distribution and are not affected by
any anomalous results. Breaking the
participants down into expert and non-expert
groups also results in similar frequency
distributions and correlates with the overall
trend (Figure 9.2).
This result suggests that both experts and non-
experts tend to agree on overall scores, but that
members of the expert group agree more
consistently.
Group Mean
Standard
Deviation
Non
Expert 0.321 1.091
Expert 0.159 0.868
Overall 0.250 1.003
Table 9.2: Group mean scores and standard deviation

Page 35 of 59
Figure 9.2: Frequency distribution of participants mean
scores
It is also possible to look at participants scores
based on their average daily video
consumption. It would be reasonable to
assume that those who are more frequently
exposed to video content would be more
susceptible to differences in video quality.
However, somewhat counter-intuitively, the
mean scores of participants in this test tended
more towards a mean of zero (neutral) with
increasing video consumption (disregarding
the 4.5≤x≤6 category, which represents an
anomalous score from only one participant)
(Figure 9.3a). This also correlates to a
decreasing deviation from their mean score
with increasing video consumption (Figure
9.3b).
It should also be noted that there are equal
proportions of experts and non-experts in all of
these groups, except for the “0≤x<1.5” group
which consists entirely of non-experts.
This suggests that those who view more video
content per day see less of a difference
between the codecs and score more
consistently with other people who consume
similar amounts of video, regardless of
whether or not the same participants identify
as experts or non-experts.
(a)
(b)
Figure 9.3: (a) Participants mean scores based on video
consumption, and the percentage of participants who
identified with each group. The graph shows more of a
tendency towards a neutral score with increasing daily
video consumption, which correlates with (b) showing
standard deviation of participants’ scores decreasing
with increasing daily video consumption.

Page 36 of 59
9.2. Differences Between Bitrates
When the results from the subjective test are
analysed from the perspective of bitrate, it is
apparent that there is no obvious trend in the
data with increasing bitrate. However, there is
a much wider variation in scores at the lowest
bitrate, 2mbps (Appendix F, Boxplot A).
This is further proved with Analysis of
Variance (ANOVA), which shows a Sphericity
Assumed P value of 0.144
. This indicates that
there is no significant evidence to suggest that
the null hypothesis (that the mean scores for
each bitrate show no significant difference) is
not true, and therefore it is reasonable to
assume that changes in bitrate have no
significant impact on the overall mean scores.
The variation in scores at 2Mbps is likely
influenced by the prevalence of encoding
errors in both codecs at such a low bitrate.
Participants may have found it more difficult
to quantify the magnitude of the differences in
errors when so many were present in both
stimuli.
9.3. Differences Between Content
When the results are analysed from the
perspective of content, it is evident that scores
are significantly impacted with differing types
of content (Appendix F, Boxplot B; and Table
9.3). This is further proved with a Sphericity
Assumed P value of less than 0.0005;
4
ANOVA looks at the differences between
group means, thus analysing the variance
amongst different groups of data. In this case
the groups being analysed are bitrates, but
Anova is also used to analyse other groups of
data further into this study. P values are
considered to indicate a significant effect when
≤0.05
indicating a very high likelihood that content
scores vary significantly.
Content Mean
Standard
Deviation
Sintel -0.131 0.546
Crowd
Run 0.032 1.280
Cobra 0.581 0.939
Clown 0.519 0.911
All 0.250 1.003
Table 9.3: Means and Standard Deviation of scores
based on content
The standard deviation for each type of
content collates with the comparable level of
difficulty each type of content presents. Sintel,
which is considered the least difficult content
to encode, has the lowest standard deviation in
scores; compared with the Crowd Run content,
which is considered the most difficult to
encode, and has the highest standard deviation
in scores. This would suggest that with
increasing content complexity, participants
were overall more uncertain about the
comparative video quality.
As the ANOVA test identifies that there is a
significant difference in the scores of different
types of content, it is appropriate to perform
post-hoc tests in the form of Paired T-tests.
The content is first put in order of means from
lowest to highest (in order to compare each
mean to the nearest mean from the other
values), giving an order of:
1. Sintel
2. Crowd Run
3. Clown

Page 37 of 59
4. Cobra
Paired T-tests between adjacent content in the
list above show where the most significant
score differences occur between content5
(table 9.4).
Content A Content B P Value
Sintel Crowdun 0.401
Crowd Run Cobra 0.006
Cobra Clown 0.126
Table 9.4: Paired T-Test results
The paired T-tests show that overall the most
significant difference in scores occurs between
the Crowd Run and Cobra content, with no
significant difference occurring between the
other compared content. This suggests that the
Sintel and Crowd Run content tend to have
significantly more neutral scores than the
Cobra and Clown content.
In context, this implies that HEVC is
considered to perform slightly better for
medium-difficulty content, but that both
codecs perform equally as well for low and
high difficulty content. This result could be
due to a number of factors, but the most likely
reason is that participants are unable to detect
differences in low difficulty content, as
demonstrated by the low standard deviation
that implies consistently neutral scores. This is
likely to due to the complexity of the content,
5
Paired T-tests compare the mean scores
between two groups measuring the same
variable, and indicate where significant
differences (that we know occur from
ANOVA results) appear between them. A P
value ≤0.05 is considered to indicate strong
evidence of a large difference in scores.
which is not difficult enough to expose any of
the potential flaws in either codec.
Due to the amount of errors produced by both
codecs with high difficulty content, it is likely
that participants cannot consistently quantify
the magnitude in quality difference, thus
producing a neutral mean score with a high
standard deviation.
In summary, despite both codecs having
neutral mean scores, caution should be taken
to ensure that the variance in scores is also
taken into consideration. The reason that the
scores for the Cobra and Clown content differ
so much from the other two may be that they
presented a complexity level where
participants were more able to identify
differences, with comparatively average
consistency. If true, this would imply that
HEVC more consistently performed slightly
better than VP9, however, it would not be
possible to reliably conclude this without
further investigation.
9.4. The Interaction Between Content
and Bitrate
Although it has been shown that bitrate did not
have a major impact on scores on its own,
boxplots of the different content at varying
bitrates reveals large differences in the
distribution of scores (Appendix F, Boxplots C
to F). The result of ANOVA testing reveals a
Sphericity Assumed P value less than 0.0005,
confirming that there is an almost certain
impact on scores from the combined variation
of bitrate and content.
9.4.1. Sintel
The boxplot for the Sintel content (Appendix
F, Boxplot C) appears to show an increasing
range of scores with increasing bitrate. The

Page 38 of 59
medians for each bitrate tend towards zero
(neutral) but in all cases, with the exception of
3Mbps, the middle 50% percentile shows a
larger tendency towards a rating of “slightly
better” for VP9; reflecting the overall mean
score of -0.131.
However, post-hoc analysis where the means
of each bitrate are ranked in ascending order,
and adjacent means are analysed using paired
T-Tests, shows that there is no significant
difference in scores with varying bitrate for the
Sintel content (Table 9.5).
Bitrate A Bitrate B P Value
2Mbps 5Mbps 0.561
5Mbps 10Mbps 0.947
10Mbps 3Mbps 0.242
Table 9.5: Sintel Paired T-Test results between bitrates
One theory is that increasing bitrate would
inevitably produce higher quality content for
both codecs, meaning that both would display
fewer compression errors on screen, which
could subsequently inhibit the capability of
participants to reliably identify differences in
quality. This effect would therefore be
accentuated in content that is unlikely to
produce many errors. This theory seems
accurate when looking at the standard
deviations in mean scores of the expert group
with increasing bitrate, but is less obvious
when looking at the non-expert group (Figure
9.4).
Figure 9.4: Standard deviation of scores for the Sintel
content with increasing bitrate, comparing the expert
and non-expert groups
In summary, there is no evidence to suggest
that the Sintel content scores were impacted by
bitrate. However, an increasing bitrate does
tend to also increase the range of results i.e.
produce more uncertainty about the
differences in quality between the two codecs.
9.4.2. Crowd Run
The boxplot for the Crowd Run content shows
a distinctive shift in scores from a preference
towards VP9 at low bitrates, to a preference
towards HEVC at higher bitrates (Appendix F,
Boxplot D); however it should be noted that
this does not take into account any of the
outlying results. It should also be reiterated
that the standard deviation for the Crowd Run
content is almost 30% higher than all content
combined (table 9.3), suggesting that there is
much more disparity in scores for this content
in comparison to others, and therefore
potentially less reliability.
Two-tailed T-Tests indicate that the only
significant difference in score occurs between
2Mbps and 3Mbps (Table 9.6).

Page 39 of 59
2Mbps 3Mbps 0.001
3Mbps 10Mbps 0.183
10Mbps 5Mbps 0.984
Table 9.6: Crowd Run two-tailed T-Test results between
bitrates
Additionally, there are no major differences in
the mean scores from expert and non-expert
groups (Figure 9.5), however the expert group
consistently gives marginally lower scores
than the non-expert group, but not
significantly so.
Figure 9.5: Crowd Run mean scores by bitrate,
comparing expert and non-expert groups
In summary, these results imply that VP9
performs better on harder content at lower
bitrates, but that HEVC is slightly better at
higher bitrates, with no notable difference
between expert and non-expert groups.
9.4.3. Cobra
The boxplot for the Cobra content indicates a
trend towards a “slightly better” score for
content at lower bitrates, and a possible trend
towards a more neutral score at higher bitrates
(Appendix F, Boxplot E). This is reinforced by
two-tailed T-tests that suggest a borderline
possibility of a significant difference between
10Mbps and 5Mbps (table 9.7).
10Mbps 5Mbps 0.051
5Mbps 3Mbps 0.264
5Mbps 2Mbps 0.252
Table 9.7: Cobra two-tailed T-test results between
bitrates. Note that the 3Mbps and 2Mbps have an equal
mean and are therefore both compared with 5Mbps
Looking at the mean scores between expert
and non-expert groups reveals large
differences at both 3Mbps and 5Mbps, but
strong correlation at 2Mbps and 10Mbps
(Figure 9.6). However, the two groups have
conflicting standard deviations with increasing
bitrate: the non-expert group shows a trend
towards increasing standard deviation;
whereas the expert group shows a trend
towards decreasing standard deviation (figure
9.7). This suggests more agreement within the
expert group with increasing bitrate, but less
agreement within the non-expert group with
increasing bitrate.
Figure 9.6: Mean scores of the expert and non-expert
groups for the Cobra content

Page 40 of 59
Figure 9.7: Standard deviation in scores of the expert
and non-expert groups for the Cobra content
It is difficult to draw any clear conclusions for
this content without further investigation, due
to the conflicting levels of agreement between
the two groups of expertise, and ambiguous
post-hoc testing results. One cautious
observation is that there does appear to be a
trend towards an overall score of “slightly
better” for HEVC, at lower bitrates, for
medium difficulty content across both groups.
9.4.4. Clown
The boxplot for the Clown content shows a
similar trend to that of the Cobra content:
scores tend more towards a score of “slightly
better” for HEVC with decreasing bitrate
(Appendix F, boxplot F). This trend is further
proved with two-tailed T-tests that show a
significant difference between 2Mbps and
5Mbps, where 2Mbps gives a significantly
higher average response in favour of HEVC
(table 9.8).
10Mbps 3Mbps 0.169
3Mbps 5Mbps 0.606
5Mbps 2Mbps 0.016
Table 9.8: Clown two-tailed T-test results between
bitrates showing a significant difference between 5Mbps
and 2Mbps
Looking at the mean scores from different
expertise groups it is clear that there is some
correlation between the two groups at 3Mbps
and 5Mbps, but less so at 2Mbps and 10Mbps
(figure 9.8). However, the standard deviations
for both groups show similar trends to those in
the Cobra content, with the standard deviation
decreasing with increasing bitrate for the
expert group, whereas the reverse tends to be
true for the non-expert group, with the
exception of 10Mbps (figure 9.9).
Figure 9.8: Mean scores of the Clown content across
expert and non-expert groups

Page 41 of 59
Figure 9.9: Standard deviation in scores of the expert
and non-expert groups for the Clown content
This suggests growing uncertainty within the
non-expert group with increasing bitrate, but
increasing certainty within the expert group. A
possible explanation for such a dramatic
difference could be that expert participants are
more likely to be capable of accurately
observing differences in content at higher
bitrates where fewer errors occur; whereas
non-expert participants become more uncertain
as the quality of both codecs increases.
Overall, both groups indicate a “slightly
better” preference towards HEVC at lower
bitrates, with a trend towards a neutral score at
higher bitrates.
9.5. Participant Comments
At the end of each test session, the participants
were asked to comment on their grading for up
to three of their most extreme scores, with the
option to re-watch the videos. Some
participants gave no comments. All of the
comments made can be found in Appendix G.
The most comments were made about the
Crowd Run contents (40.9%), followed by the
Clown content (31.8%), the Cobra content
(18.2%), and the Sintel content (9.1%).
Interestingly, these percentages correlate to the
relative complexity of each video, suggesting
that participants found the most distracting
types of errors occurred in the hardest content.
However, they do not correlate to the overall
mean scores discussed in 8.3. This could
insinuate that although participants found the
most distracting errors in the hardest content,
those errors occurred almost equally across
both codecs.
Unsurprisingly, over two-thirds of the
comments made were in relation to either the
2Mbps or 3Mbps bitrates (36.4% each), where
most errors are expected to occur, with the
remaining comments made about the 5Mbps
bitrate (27.3%). Similarly, this would suggest
that the most distracting types of error were
also found at the lowest bitrates. This is
supported by the much wider variance in
scores at low bitrates discussed in chapter 8.2,
which implies that those errors were present in
both codecs.
The most common complaints are in relation
to “blockiness” and issues with colour, which
implies that these are the most distracting
errors.
9.6. Summary
In summary, scores were affected by all
variables to some degree, but the most impact
appears to come from content, and the
interaction between content and varying
bitrate. Other key points are as follows:
• Expert and non-expert participant
groups overall show reasonable
agreement in scores, but experts show
more correlation in their scores

Page 42 of 59
• Participants who watch more video
content per day are less likely to see
differences between the two codecs
• Bitrate alone shows no significant
impact on scores
• There is strong evidence to suggest that
content, and varying bitrate within
content, has the most substantial
impact on scores
• Participants comments suggest that the
most distracting errors are colour
issues and “blockiness”
10. CONCLUSION
Looking at the features they employ it is clear
that both codecs have been appropriately
developed with current consumer level
hardware in mind, where increasing power and
number of processor cores available allow
decoding devices to handle greater complexity
and parallelisation. An increase in the
complexity of each codec and the advanced
parallelisation tools that play such a major part
in them both shows a clear progression from
previous standards. HEVC demonstrates
advancements upon its predecessor AVC in
the form of refining its features to make them
more complex and more efficient, as well as
implementing completely new features such as
WPP and a new quadtree coding structure;
VP9, despite being the newcomer to the
compression industry, introduces new
techniques such as ARFs and ADST
transforms, without access to patents that
would have previously been considered
essential, and has done so largely through
open-source community development.
The subjective testing conducted in this study
shows very little difference between the
codecs, with the overall score laying within the
“equal” bracket of measurement. However,
there are differences between the codecs on
differing content, where HEVC produces a
score of “slightly better” for medium-difficulty
content in comparison to VP9, but both codecs
perform equally for low and high complexity
content. Looking closer at the relationship
between bitrate and content, HEVC shows a
much clearer advantage at lower bitrates for
medium-difficulty content, but there is a trend
towards a more neutral score with increasing
bitrate. On the contrary, VP9 shows more of
an advantage for high-difficulty content at low
bitrates, but loses preference to HEVC with
increasing bitrate.
Unsurprisingly, participants who identify as
experts show more correlation in their scores,
and are therefore perhaps more reliable,
however, the overall trend for non-experts
tends to follow the same pattern. This is an
important result as it confirms that non-expert
consumers, who will make up the vast
majority of the target market, see differences
between the codecs in some areas which could
influence the decision of which codec vendors
are likely to use depending on the content they
are producing and the bitrate they are
distributing it at.
The results of these subjective tests, therefore,
largely mirrors those of other comparative
tests between the codecs in that there is no
conclusive evidence to suggest an overall
advantage of either codec.
It is likely then that one of the largest
influences on vendors to choose either codec
will ultimately be a financial one. However,
the strong reputation and rich history of
HEVC’s predecessors may give it an

Page 43 of 59
advantage over VP9, which, realistically, will
need to prove its reliability and practicality in
the near future in order to become a viable
competitor.
11. FURTHER WORK
This study compared the encoders using
medium-complexity settings, it would be
beneficial to see the differences between the
codecs when they are both using their best,
and most complex, settings. However, to be at
all viable, much more powerful equipment
would be needed for the encoding and
decoding processes. Similarly, more powerful
hardware would allow for a comparison
between compressed and uncompressed video,
which would produce more credible results.
Alternatively, it would also be beneficial to see
how each codec performs using low
complexity profiles designed for the streaming
of video to mobile devices, where a decrease
in bitrates represents a more direct decrease in
cost to the consumer.
Finally, another study, when both codecs have
been fully ratified, and their documentation
fully released, will allow for a more
transparent comparison and give a greater
indication of the advantages of each.

Page 44 of 59
12. REFERENCES
Benoit. H. (2002). Digital Television: MPEG-1, MPEG-2 and principles of the DVB system.
Focal Press.
British Standards Institute (1993). Coding of moving pictures and associated audio for digital
storage at up to about 1.5 Mbit/s – Part 2: Video. (BS EN ISO/IEC 11172-2).
Chi, C., Alvarez-Mesa, M., Juurlink, B., Clare, G., Henry, F., Pateux, S., and Schierl, T. (2012).
Parallel Scalability and Efficiency of HEVC Parallelization Approaches. IEEE Transactions on
Circuits and Systems for Video Technology, Vol. 22 (12), P.1827-1838. DOI:
10.1109/TCSVT.2012.2223056
Cornsweet, T.N. (1970). Visual Perception. Academic Press Inc.
Davson, H. (1980). Physiology of the Eye (4th
ed.). Palgrave Macmillan
Delargy, J., Wendt, P.(1996). A performance comparison of three software video codecs for IBM
RISC System/6000 workstations. Signal Processing-image Communication, Vol.8(1), 39-44.
doi:10.1016/0923-5965(95)00015-1
Digital Video Broadcasting (DVB) (2014). Specification for the use of Video and Audio coding
in Broadcasting Applications based on the MPEG-2 Transport Stream. (DVB Document A157).
Retrieved from https://www.dvb.org/resources/public/standards/a157_dvb-avc-mpeg2.pdf
Ferguson, T. (2001). Retrieved 2014, 3rd
November from http://multimedia.cx/mirror/cinepak.txt
Ffmpeg. (n.d). H.265 Encoding Guide. Retrieved April 2015 from
https://trac.ffmpeg.org/wiki/Encode/H.265
Ghanbari, M. (1999), Video Coding, IEEE
Ghanbari, M. (2011). Standard Codecs: Image Compression to Advanced Video Coding (3rd
Ed.). IEE.
Grange, A., Alvestrand, H. (2013). A VP9 Bitstream Overview. Google.
Grois, D., Marpe, D., Mulayoff, A., and Hadar, O. (2013). Performance Comparison of
H.265/MPEG-HEVC, VP9, and H.264/MPEG-AVC Encoders. 2013 Picture Coding Symposium,
P.394-7. doi:10.1109/PCS.2013.6737766
Hardy, A.C. (1919). An Experimental Study of the Persistence of Vision
Heo, J., Kim, S., Ho, Y. (2010). Improved CAVLC for H.264/AVC Lossless Intra Coding. IEEE
Transactions on Circuits and Systems for Video Technology, Vol. 20(2), 213-222. DOI:
10.1109/TCSVT.2009.2031392
Ibrahim, K.F.(2007), Newnes guide to Television & Video Technology (4th
Ed.), Newnes

Page 45 of 59
International Telecommunications Union (ITU) (1988). Video Codec for Audiovisual services at
Px64 Kbits. (ITU-T Rec. H.261)
International Telecommunications Union (ITU) (1995). Generic Coding of Moving Pictures and
Associated Audio Information: Video. (ITU-T Rec. H.262)
International Telecommunications Union (ITU) (2012). Methodology for the Subjective
Assessment of the Quality of Television Pictures. (ITU-R BT.500-13).
International Telecommunications Union (ITU) (2013). Advanced Video Coding for Generic
Audiovisual Services (8th
Ed.). (ITU-T Rec. H.264)
Ishihara, S. (1917). Test for Colour-Blindness
Kapsenberg, P. (2013). How VP9 works, technical details, and diagrams. Retrieved from
http://forum.doom9.org/showthread.php?t=168947
Landis, C. (1954). Determinants of the Critical Flicker-Fusion Threshold
Mathematicalmonk. (2011). Arithmetic Coding - Example #1. [Online Video]. 08 October 2011.
Retrieved from: https://www.youtube.com/watch?v=7vfqhoJVwuc. Accessed: 01 December
2014.
Misra, K., Segall, A., Horowitz, M., Xu, S., Fuldseth, A., Zhou, M. (2013). An Overview of
Tiles in HEVC. IEEE Journal of Selected Topics in Signal Processing, Vol. 7 (6), 969-977. DOI:
10.1109/JSTSP.2013.2271451
Mukherjee, D., Bankoski, J., Grange, A., Han, J., Koleszar, J., Wilkins, P., Xu, Y., Bultje, R.
(2013). The Latest open-source video codec VP9 – An overview and preliminary results. 2013
Picture Coding Symposium, P.390-93. DOI: 10.1109/PCS.2013.6737765
Panasonic. (n.d). Specification for Panasonic TX-58AX802B. Retrieved April 28th
2015 from
http://www.panasonic.com/uk/consumer/viera-televisions/led/tx-58ax802b.specs.html
Ramamoorthy, R (Google Developers, 2014, June 25th
). Update on WebM/VP9. Retrieved from
https://www.youtube.com/watch?v=xo_R40C7RTo
Remington, L.A. (2012). Clinical Anatomy and Physiology of the Visual System (3rd
ed.).
Butterworth-Heinemann
Rerabek, M. and Ebrahimi, T. (2014). Comparison of compression efficiency between
HEVC/H.265 and VP9 based on subjective assessments. Applications of Digital Image
Processing, Vol. 9217. doi:10.1117/12.2065561.
Richardson, I.E.G (2003), H.264 and MPEG-4 – Video Coding for Next-generation Multimedia.
Wiley.
Seabrook, G.R.(1989). Arithmetic Coding – An alternative VLC strategy for video coding. Third
International Conference on Image Processing and its Applications, P.613-17. IET.
Segaretro (2014), Retrieved 2014, 3rd
November from http://segaretro.org/Cinepak

Page 46 of 59
Sullivan, G.J., Ohm, J., Han, W., Wiegand, T. (2012). An Overview of the High Efficiency
Video Coding (HEVC) Standard. IEEE Transactions on Circuits and Systems for Video
Technology, Vol. 22 (12), 1649-68. DOI: 10.1109/TCSVT.2012.2221191
The Webm Project. (n.d.) Encoder Parameters. Retrieved April 1st
2015 from
http://wiki.webmproject.org/ffmpeg/vp9-encoding-guide
The Webm Project. (n.d.). Retrieved March 31st
, 2015 from
http://www.webmproject.org/about/faq/
Watkinson, J.(2004) The MPEG Handbook (2nd
ed.), Elsevier
Watkinson, J.(2008), The Art of Digital Video 4th
Edition, John Watkinson, Focal Press
Wilkins, P. (8th May 2013). VP9 Bitstream finalization update. WebM Project. Google.
X265. (n.d). X265 Preset Options. Retrieved April 2015 from
http://x265.readthedocs.org/en/default/presets.htm
Zhang, S., Zhang, X., Gao, Z. (2014). Implementation and improvement
of Wavefront Parallel Processing for HEVC encoding on many-core platform. Presented at IEEE
International Conference on Multimedia and Expo. DOI: 10.1109/ICMEW.2014.6890596

Page 47 of 59
Appendix A: Table of defined MPEG-2 Profiles and Levels
Profiles
Simple Main 4:2:2 SNR Spatial High
Levels
High
4:2:0
1920x1152
90Mb/s
4:2:0 or
4:2:2
1920x1152
100Mb/s
High
1440
4:2:0
1440x1152
60Mb/s
4:2:0
1440x1152
60Mb/s
4:2:0 or
4:2:2
1440x1152
80Mb/s
Main
4:2:0
720x576
15Mb/s
No B
4:2:0
720x576
15Mb/s
4:2:2
720x608
50Mb/s
4:2:0
720x576
15Mb/s
4:2:0 or
4:2:2
720x576
20Mb/s
Low
4:2:0
352x288
4Mb/s
4:2:0
352x288
4Mb/s
Appendix B: Table of maximum number of enhancement layers for each MPEG-2
scalable profile (International Telecommunications Union, 1995)

Page 48 of 59
Appendix C: The different directional modes of Intra 4x4 coding (Retrieved from
http://people.xiph.org/~xiphmont/demo/daala/intramodes.png)

Page 49 of 59
Appendix D: HEVC picture partitioning compared with H.264 picture partitioning
Appendix E: Subjective Testing Encoder Commands
VP9:
ffmpeg -y -i <input> -c:v libvpx-vp9 -pass 1 -b:v <bitrate> -
deadline good -speed 4 -tile-columns 6 -frame-parallel 1 -auto-
alt-ref 1 -rc_lookahead 25 -g 250 -keyint_min 25 -
arnr_max_frames 7 -arnr_strength 5 -an -f webm /dev/null &&
ffmpeg -y -i <Input> -c:v libvpx-vp9 -pass 2 -b:v <Bitrate> -
deadline good -speed 2 -tile-columns 6 -frame-parallel 1 -auto-
alt-ref 1 -rc_lookahead 25 -g 250 -keyint_min 25 -
arnr_max_frames 7 -arnr_strength 5 -an -f webm <output>.webm
HEVC:
ffmpeg -y -i <input> -c:v hevc -pass 1 -x265-params
ref=4:bframes=16:b-adapt=2 -b:v <bitrate> -preset medium -
pix_fmt yuv420p -f mp4 /dev/null && ffmpeg -y -i <input> -c:v
hevc -pass 2 -x265-params ref=4:bframes=16:b-adapt=2 -b:v
<bitrate> -preset medium -pix_fmt yuv420p -f mp4 <output>.mp4

Page 50 of 59
Appendix F: Boxplots of results
(A)
(B)

Page 51 of 59
(C)
(D)

Page 52 of 59
(E)
(F)

Page 53 of 59
Appendix G: Test Participants Comments

Page 54 of 59
Glossary of Important Terms and
Equations
Progressive and Interlaced scanning
These are the two scanning methods used by
televisions to reproduce video.
Interlaced scanning is a scanning method
inherited from analogue where each frame is
split into two fields. The first field contains
all of the information for the odd horizontal
lines, and the second field contains the
information for the even horizontal lines.
The two fields are displayed in quick
succession to show the full frame. This
system was used in analogue to save
bandwidth, as only half the data rate needed
to display the equivalent video scanned
progressively, is required.
Progressive scanning is a scanning method
where the entire frame is displayed at once.
This is generally preferred to interlaced
scanning, in terms of quality, as it does not
produce the same artefacts on the screen that
are sometimes seen with interlaced scanning
for video with fast moving action.
Lossless and Lossy Compression
Lossless compression reproduces identical
data at the output of the decoder, to what
was seen at the input of the encoder. This is
done in a variety of ways to ensure that data
is stored in the most efficient way possible
and all redundant data is removed.
Lossless compression methods cannot
guarantee a particular compression ratio as it
depends how much redundant information is
present. However, in general, lossless
compression is limited to a compression
ratio of around 2:1 (Watkinson, 2004).
Lossy compression will not reproduce
identical data at the decoder to that seen at
the input of the encoder. Lossy compression
methods introduce errors into the data
stream and try to arrange them, using
psychovisual techniques, so that they are as
unperceivable as possible to the viewer. This
type of compression offers much higher
compression ratios and is used in most
broadcast applications.
Sampling
The eye is more sensitive to green than it is
to red and blue, therefore samples in a video
signal are taken of chrominance red (Cr) and
chrominance blue (Cb); with the green
portion being calculated by subtracting the
Cr and Cb values from the Y value. This is
so that the lowest amount of data possible
can be used to store the full luminance and
chrominance data of the image. The
resulting signal is commonly written as
YCrCb, but in digital video is also sometimes
referred to as YUV.
When video is recorded or displayed, the
luminance and chrominance (colour)
samples are not recorded in equal
proportions, as the eye is more sensitive to
changes in luminance, therefore there are
various sample rates that can be used to
carry colour information in varying degrees
of accuracy.
The most common sampling rates are 4:2:2
and 4:2:0.

Page 55 of 59
4:2:2 Sampling
This sampling rate takes two red and two
blue samples at the same point, for every
four luminance samples (Figure X.1). Doing
this reduces the horizontal colour
information but maintains all vertical colour
information.
Figure X.1: 4:2:2 Sampling (Ibrahim, 2007)
4:2:0 Sampling
In 4:2:0 sampling, two red (Cr) and blue (Cb)
samples are taken for every four luminance
samples but only on alternating horizontal
lines (Figure 2.2). This results in alternating
lines that contain luminance information
only but maintains a balance in vertical and
horizontal resolution (Ibrahim, 2007).
However, 4:2:0 sampling only records half
of the colour information that 4:2:2
sampling records.
Figure X.2: 4:2:0 Sampling (Ibrahim, 2007)
Quantisation and Bit Depth
After an image has been sampled, the values
are then quantised. Quantisation rounds a
value up or down to the nearest integer; the
bit depth dictates the number of integers
(Ibrahim, 2007).
Bit depth is described mathematically as
powers of two. A bit depth of eight bits can
represent two hundred and fifty six values;
whereas a bit depth of ten can represent one
thousand and twenty five values (Equation
X.1).
Equation X.1: Bit depth examples
Bit Rate
If the parameters used to encode the video
are known it is possible to calculate the bit
rate of the resulting video bit stream6
. The
6
It is important to note that there are 8 bits
per byte of data. This can cause a lot of
confusion and it is important to recognize
which unit is being used.
€
28
= 256
210
=1025

Page 56 of 59
equation for luminance only (monochrome)
video is simply the number of samples per
second, multiplied by the number of bits per
sample (Table X.1). The equation for colour
video also takes into account the sampling
rate and adds the bit rates for Cr and Cb to
the luminance bit rate (Equation X.2). These
equations give the values for uncompressed
digital video, and therefore demonstrate the
need for further compression for efficient
video storage and transmission.
Value
Source

Bit
Rate
Samples
per
second
x

no.
bits
per
sample

Samples
per
second
no.
samples
per
frame

x
no.
frames
per

second

Samples
per
frame
Resolution

Table X.1: Bit rate calculation
Equation X.2: Bit rate calculation for a
1920x1080p50 4:2:2 video stream.
Inter and Intra coding
Watkinson (2004, P.15) states that “Video
signals exist in four dimensions: these are
the attributes of the pixel, the horizontal and
vertical spatial axes, and the time axis”. It is
possible to compress any of these
dimensions in order to reduce the video bit
rate.
The four dimensions can be split into two
types of coding: intra (or spatial) coding,
which encodes the pixel attributes,
horizontal, and vertical axes; and inter (or
temporal) coding, which encodes the times
axis. Put simply, intra-coding aims to reduce
the redundancy within one frame; and inter-
coding aims to reduce redundancy between
frames by encoding the difference between
the pixels in the current frame, and the
predicted ones from reference frames.
Discrete Cosine Transform
The Discrete Cosine Transform (DCT)
(Equation X.3) is a commonly used, Fourier
Transform based, technique, used for spatial
coding. It converts the waveform into the
frequency domain where redundancy can be
easily detected and removed (Watkinson,
2004). Each nxn pixel block within the
image is represented as a summation of the
frequency of DCT basis functions (Figure
X.3) that appear within the block. By doing
this, the distribution of the varying pixel
values within a block can be represented
using very little data.
€
Y =1920 ×1080 × 50 × 8
= 829,440,000 Bits per second
= 829.44 Mega Bits per second
Cr = 960 ×1080 × 50 × 8
= 414,720,000 Bits per second
= 414.72 Mega Bits per second
Cb = Cr
Y + Cr + Cb = 829.44 + 414.72 + 414.72
=1.66 Giga Bits per second
=1.66 ÷ 8 = 207.36 Mega Bytes per second

Page 57 of 59
Equation X.3: The Discrete Cosine Transform
Figure 2.3: DCT Basis Functions. For example, if a
pixel block mostly contained pixels arranged in
horizontal bars, the most frequent basis function that
appeared in the transform would be those appearing
in the left hand column of the image above. Most
other data relating to other basis functions can be
discarded (if the compression method is lossy), but
the pixel block can be accurately reproduced using
minimal information.
A common method of spatial coding is
analyzing an 8x8 pixel block within a frame
and transforming the values using the DCT.
The encoder may discard any zero or near-
zero values and quantize the remaining
values. The decoder can use this information
to efficiently represent the value of each
pixel within the block based on the
frequency of DCT basis functions (Ibrahim,
2007, P.50). By transforming the pixel
block, the decoder is able to make a
reasonable assumption of the value of each
individual pixel, whilst maintaining a low
Mean Square Error (the measure of how
much error is introduced after a transform).
Entropy Coding
Entropy is the minimum number of binary
bits needed to represent the information in a
data stream. Claude Shannon first calculated
the theoretical limit of this in his 1948 paper
“A Mathematical Theory of
Communication” (Equation 2.4).
Equation 2.4: Shannon’s equation for entropy coding.
Where Pi is the probability of a given symbol i, and
N is the number of symbols
One of the best, and most frequently used,
types of entropy coding is Huffman coding.
This can be applied after the DCT to arrange
values in a tree format, where values are
sorted into nodes. The two nodes with the
lowest probability are placed at the bottom
of the tree and combined into a single node
with the sum of their probabilities. Each pair
of branches is then separately assigned a 0
or 1. The code for any given value is
calculated by working up the tree from the
values node, combining the 0’s and 1’s from
the branches used to get to the top of the tree
(Figure 2.4). This method of entropy coding
satisfies the prefix property, meaning that
the code for any given value is not the prefix
for the code for another value; avoiding any
ambiguity when the code is decoded.
€
F u,v( ) =
Cu
2
Cv
2 y=0
n−1
∑ f (x,y)cos
x=0
n−1
∑
(2x +1)uπ
2n
%
&'
(
)* • cos
(2y +1)vπ
2n
%
&'
(
)*
Where :
Cu =
1
2
if u = 0, or 1 if u ≠ 0
Cv =
1
2
if v = 0, or 1 if v ≠ 0
n = Number of pixels
€
Entropy = pi log2 pi
i=1
N
∑

Page 58 of 59
Figure X.4: Example of a Huffman tree with the
codes for each symbol shown in the table below.
The DCT is coupled with Variable Length
Coding (VLC) and Run Length Coding
(RLC), which code the most common value
with the lowest word length (The bottom-
right pixel in the right block from Figure
X.3). This is done using Huffman coding
tables, which are a form of algorithm that
determine the number of bits assigned to
each value.
DCT and Entropy Coding example
This example demonstrates how the DCT
and entropy coding are combined to reduce
the amount of data that is coded.
Step one: Sample the picture and record
pixel values
Step two: Convert the values using the DCT
Step three: Quantise the values using a
quantisation matrix
Step four: Using entropy coding, efficiently
code the matrices so that it can be
represented using the least number of bits.

Page 59 of 59
Most importantly, this will efficiently
encode all of the zero values in the matrix so
that they consist of as few bits as possible.
Motion Compensation
Motion compensation is a form of temporal
coding that encodes the movement of a
pixel, or block of pixels, in the form of a
motion vector, between frames so that the
decoder can predict its position in the
following frame.
Motion compensation is the primary method
used for inter-frame compression.

Jacob King Final Year Project

More Related Content

Viewers also liked

Similar to Jacob King Final Year Project

Jacob King Final Year Project