Electronic Bazaar is impelling EOS C300 Cinema Camera Body , Featuring an ascent Super 35mm Canon CMOS sensor, dynamic Canon DIGIC DV III Image Processor and 50 Mbps 4:2:2 codec, the EOS C300 gives striking film quality motion picture get, Price included GST and 7 days transport time.
HDR and WCG Video Broadcasting Considerations.pdfssuserc5a4dd
Elements of High-Quality Image Production–Color Gamut Conversion (Gamut Mapping and Inverse Gamut Mapping)–Gamma, OETF, EOTF, OOTF, PQ and HLG HDR–HDR & SDR Mastering, Mapping, Tone Mapping and Inverse Tone Mapping–Static and Dynamic Tone Mapping–Backwards Compatibility–HDR and WCG Production Equipment –DVB UHD Phases–HDR Metadata and HDR Standards –PQ10 and HLG10 Distrib
Electronic Bazaar presents Canon EOS C100 Dual Pixel CMOS AF Camera body, The EOS C100 utilizes the same Super 35mm Canon CMOS sensor and DIGIC DV III picture processor as its enormous sibling, the C300.with free dispatching, Price included GST.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/01/cmos-image-sensors-a-guide-to-building-the-eyes-of-a-vision-system-a-presentation-from-gopro/
Jon Stern, Director of Optical Systems at GoPro, presents the “CMOS Image Sensors: A Guide to Building the Eyes of a Vision System” tutorial at the September 2020 Embedded Vision Summit.
Improvements in CMOS image sensors have been instrumental in lowering barriers for embedding vision into a broad range of systems. For example, a high degree of system-on-chip integration allows photons to be converted into bits with minimal support circuitry. Low power consumption enables imaging in even small, battery-powered devices. Simple control protocols mean that companies can design camera-based systems without extensive in-house expertise. Meanwhile, the low cost of CMOS sensors is enabling visual perception to become ever more pervasive.
In this tutorial, Stern introduces the basic operation, types and characteristics of CMOS image sensors; explains how to select the right sensor for your application; and provides practical guidelines for building a camera module by pairing the sensor with suitable optics. He highlights areas demanding of special attention to equip you with an understanding of the common pitfalls in designing imaging systems.
Hitachi 4k studiová 2/3" kamera nové generacePetr Krkavec
Prezentace nové profesionální 2/3" studiové kamery Hitachi SK-UHD4000. Vysvětlení konfigurace celého kamerového řetězce a použití technologi čtyř MOS čipů.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
2. – Part I
Elements of High-Quality Image Production
– Part II
4K and 8K in Modern Broadcast Cameras
– Part III
Modern Broadcast Camera Technology
– Part IV
HDR and WCG in Modern Broadcast Cameras
2
OUTLINE
4. Q1 Spatial resolution (HD, UHD)
Q2 Temporal resolution (Frame Rate) (HFR)
Q3 Dynamic Range (SDR, HDR)
Q4 Color Gamut (BT. 709, BT. 2020)
Q5 Component Coding (Quantization, Bit Depth)
Q6 Compression artifacts
.
.
.
Total Quality of Experience (QoE or QoX) = F(Q1, Q2, Q3, … Qn)
Not only more pixels, but better pixels
4
Elements of High-Quality Image Production
5. UHDTV 1
3840 x 2160
8.3 MPs
Digital Cinema 2K
2048 x 1080 2.21 MPs
4K
4096 x 2160 8.84 MPs
SD (PAL)
720 x 576
0.414MPs
HDTV 720P
1280 x 720
0.922 MPs
HDTV 1920 x 1080
2.027 MPs
UHDTV 2
7680 x 4320
33.18 MPs
8K
8192×4320
35.39 MPs
Wider viewing angle
More immersive
Q1:
Spatial Resolution
8. – Deeper Colors
– More realistic pictures
– More Colorful
– Rec. 2020 color space covers 75.8%, of CIE 1931 while
Rec. 709 covers 35.9%.
Wide Color Space (ITU-R Rec. BT.2020)
Color Space (ITU-R Rec. BT.709)
Q3:
Wide Color Gamut
WCG
9. Images : Dolby Laboratories
Standard Dynamic Range
High Dynamic Range
(More Vivid, More Detail)
Q4:
High Dynamic Range
11. 11
Q3+Q4:
Wide Color Gamut + High Dynamic Range
SDR
SDR
HDR
HDR+WCG
More vivid, More details
More real, More colorful
12. 12 bits
4096 Levels
10 bits
1024 Levels
8 bits
256 Levels
12
– Wide Color Gamut Makes Deeper Colors
Available
– With more colours to represent, higher bit sample
rates (10-bit) are critical
Q5:
Quantization (Bit Depth)
13. Spatial Resolution (Pixels)
HD, FHD, UHD1, UHD2
Temporal Resolution (Frame rate)
24fps, 30fps, 60fps, 120fps …
Dynamic Range (Contrast)
From 100 nits to HDR
Color Space (Gamut)
From BT 709 to Rec. 2020
Quantization (Bit Depth)
8 bits, 10 bits, 12 bits …
13
Five Major Elements of High-Quality Image Production
14. Next Gen Audio
WCG
HDR
New EOTF
HFR (> 50 fps)
Screen Size
4K Resolution
0 1 2 3 4 5 6 7 8 9 10
14
Added Value Score/ Importance
Source: Ericsson Co. 2015 and Ultra HD Froum
What’s Important in UHD
23. – 2016 Rio Olympic Games (The world's first 8K live broadcast)
– 2020 Tokyo Olympics in 8K (NHK)
23
8K Motivation
24. – 35 mm Film Process:
– Expensive
– Slow
– Inflexible
– Manual process for assembling shows
– 4K emulates the resolution of 35mm Film
– The cost of film processing makes 4K attractive
– Film prints get scratched and dirty after only a few plays, 4K D Cinema keep a pristine image at all times.
– TV Broadcasting with Q1, Q2, Q3, Q4 and Q5
24
4K Motivation
26. 4 × HD (3840×2048 or 2(1920×1080))
Image Size
26
27. UHD is More Immersive
@ Proper Viewing Distance for each format
Wider viewing angle
More immersive
27
28. Delivery of 4K HDR
HDR Support Status
IFA 2017
– 4K Ultra Blu-ray
– Netflix
– Amazon
– VUDU
– YouTube Red
– UltraFlix
– PlayStation Video
– ULTRA
– Fandango
– Google play
– DirecTV
– Dish
– Xfinity
28
30. 30
Base Band Bit rate Comparison
1.5
3.0 3.0
12.0
48.0
-
10.0
20.0
30.0
40.0
50.0
60.0
FULL HD
1920× 1080, 50i
FULL HD
1920×1080, 50p
FULL HD
1920×1080, 50p
(HDR+WCG)
4K/UHD1
(HDR+WCG)
8K/UHD2
(HDR+WCG)
1.5Gb/s
3Gb/s 3Gb/s
12Gb/s
48Gb/s
Gb/s
Belden 1855
46m
Belden 1855
52m
Belden 1855
52m
Belden 1855
87m
Belden 1855
46m
Belden 7731
106m
Belden 7731
106m
31. 31
Price Comparison for Video Equipment of one Studio
$510,000 $538,000 $553,000
$730,000
$2,920,000
$0
$500,000
$1,000,000
$1,500,000
$2,000,000
$2,500,000
$3,000,000
$3,500,000
FULL HD
1920× 1080, 50i
FULL HD
1920×1080, 50p
FULL HD
1920×1080, 50p
(HDR+WCG)
4K/UHD1
(HDR+WCG)
8K/UHD2
(HDR+WCG)
• 4 Studio Cameras
• Lenses
• Pedestals
• Video Mixer
• Router
• MVs
• Recorders/Players
• Digital Glues
• …
37. Red, Green & Blue Components
37
– Colour pictures can be broken
down into three primaries.
Red Green Blue.
– Original plan to use these
primaries in colour television.
– The colour are called
components.
40. Pedestal/Master Black
40
Sut-up Level
─ The absolute black level or the darkest black that can be reproduced by the camera.
─ Base reference for all other signal levels
─ The pedestal can be adjusted as an offset to the set-up level.
Blackish and heavier
Foggy with less contrast
41. F-stop
F-stop indicates:
The amount of incident light at smaller iris openings.
Notes:
F-stops are calibrated from the lens’s widest iris opening to its smallest, however the diameter (D)
being that for the given iris opening.
F-stops are a global reference for judging the amount of light that should be allowed through the lens
during a camera shoot.
41
42. F-stop
42
─ F-stop calibrations increase by a factor of root 2, such as 1.4, 2, 2.8, 4, 5.6, 8, 11, 16, and 22
─ As the value of the F-stop increments by one step (e.g., 5.6 to 8.0), the amount of light passing
through the lens decreases by one half.
43. Fractional stops
The one-stop unit is also known as the EV (exposure value) unit.
To calculate the steps in a full stop (1 EV) one could use
20×0.5, 21×0.5, 22×0.5, 23×0.5, 24×0.5 etc.
The steps in a half stop (1/2 EV) series would be
20/2×0.5, 21/2×0.5, 22/2×0.5, 23/2×0.5, 24/2×0.5 etc.
The steps in a third stop (1/3 EV) series would be
20/3×0.5, 21/3×0.5, 22/3×0.5, 23/3×0.5, 24/3×0.5 etc.
43
45. Lens Flange-Back Adjustment Procedure
1- Set the camera and lens as follows:
-Turn the lens FB adjustment knob to 0.
-Place a Siemens star chart at an 3m for a studio or ENG lens, and 5 to 7 m for an outdoor lens.
-Open the lens to full aperture
2- Turn the lens to the telephoto end of the zoom.
3- Turn the focusing ring to bring the image into focus.
4- Turn the lens to the wide-angle end of the zoom.
5- Loosen the FB adjustment lock and turn the adjustment ring until the green channel is in sharp focus.
6- Repeat steps 2 to 5 several times, until the image is in focus at both ends of zoom.
7- Turn the lens to the wide-angle end of the zoom and check the focus on the red and blue channels. If red or blue focus is
unsatisfactory, turn the FB adjustment ring back to 0 and perform tracking readjustment.
45
48. Flare
– Flare is caused by numerous diffused (scattered) reflections of the incoming light within the camera lens.
– This results in the black level of each red, green, and blue channel being raised, and/or inaccurate color balance
between the three channels.
48
50. CCD Imager WF MonitorIris
H
H
V
V
50
Ideal Lens
Real Lens
51. Flare
– On a video monitor, flare causes the picture to appear as a misty (foggy) image, sometimes with a color shade.
– In order to minimize the flare effect:
A flare adjustment function is pprovided, which optimizes the pedestal level and corrects the balance between the three
channels electronically.
51
Test card for overall flare measurement Test card for localized flare measurement
52. Master Flare Function
The Master FLARE function enables one VR to control the level of the master FLARE with keeping the tracking of all R/G/B
channels. This feature makes it possible to control during operation since the color balance is never off.
52
53. White Shading
─ Shading is any horizontal or vertical non-linearity introduced during the image capture.
─ White shading
─ It is a phenomenon in which a green or magenta cast appears on the upper and lower parts of the screen, even when
white balance is correctly adjusted in the screen center (Two reasons).
─ The color-filtering characteristics of each prism slightly change according to the angle that the light enters each reflection layer
(incident angle).
─ Lens’s uneven transmission characteristics.
53
57. Black Shading
– Black shading is a phenomenon observed as unevenness in dark areas of the image due to dark current noise of the
imaging device.
– A black shading adjustment function is available to suppress this phenomenon to a negligible level.
Dark current noise:
The noise induced in a CCD by unwanted electric currents generated by various secondary factors, such as heat
accumulated within the imaging device.
57
58. We have two kinds of aberration:
“Axial chromatic aberration” or “Longitudinal chromatic aberration”
“Lateral chromatic aberration” or “Chromatic difference of magnification”.
(In the actual video image, this appears as color fringing around color borders)
58
Chromatic Aberration
59. When light passes through glass, the path it follows gets bent. This phenomenon is called refraction. The angle of refraction
depends on the light’s wavelength, which determines its color.
– This fact also holds true for the lenses used in a video camera lens.
– If one color is in focus on the CCD imager , other colors will be slightly out of focus.
– Less chromatic aberration provide sharper images and are generally more expensive.
Both axial chromatic aberration and lateral chromatic aberration become more noticeable in lenses with longer focal
lengths . This results in the deterioration of picture edges.
– Video camera lenses used today are designed with considerations to reduce such chromatic aberrations.
– This is achieved by combining a series of converging and diverging lenses with different refraction characteristics.
– The use of crystalline substances such as fluorite is also an effective means of reducing chromatic aberration.
59
Chromatic Aberration
63. 63
Permissible Circle of Confusion
– In optics, a circle of confusion is an optical spot caused by a cone of light rays from a lens not coming to a perfect focus when
imaging a point source.
– If an image is out of focus by less than the “Permissible Circle of Confusion”, the out-of-focus is undetectable.
64. Permissible Circle of Confusion & Effect of the Image Sensor
– The Permissible Circle of Confusion is re-defined by the sampling of the image sensor.
– The permissible Circle of Confusion is the distance between two sampling lines.
– For the Super 35mm lens, the vertical height is 13.8 mm.
– For the 2/3” lens, the vertical height is 5.4 mm.
64
65. Depth of Field
When focusing a lense on an object, there is a certain distance range in front of and behind the focused object that also
comes into focus.
– This range is called depth of field and indicates the distance between the closest and furthest points that comes into
focus under the same focus adjustement.
– When this distance is long ,the depth of field is deep.
– When this distance is long ,the depth of field is shallow.
65
66. It is governed by the three following factors:
I. The larger the iris (F-number & F-stop) , the deeper the
depth of field.
II. The shorter the lens’s focal length , the deeper the depth of
field.
III. The further the distance between the camera and the
subject, the deeper the depth of field.
– Depth of field can therefore be controlled by changing
these factors, allowing camera operators creative
expression.
– For example: A shallow depth of field is used for shooting
portraits, where the subject is highlighted and the entire
background is blurred.
66
Depth of Field
67. The different light source types emit different colors of light (known as color spectrums) and video cameras capture this
difference.
67
Color Temperature
68. Color Temperature
– Our eyes are adaptive to changes in light source colors – i.e., the color of a particular object will always look the
same under all light sources: sunlight , halogen lamps, candlelight, etc.
– When shooting images with a color video camera, it is important for the camera to be color balanced according to
the type of light source (or the illuminant) used.
– This is because different light source types emit different colors of light (known as color spectrums) and video
cameras capture this difference.
68
The camera color temperature is lower
than environment color temperature
The camera color temperature is upper
than environment color temperature
69. Color Temperature
– Color temperature is used to describe the spectral distribution of light emitted from a light source.
– Cameras do not automatically adapt to the different spectrums of light emitted from different light source types.
– In such cases, color temperature is used as a reference to adjust the camera’s color balance to match the light
source used.
For example, if a 3200K (Kelvin) light source is used, the camera must also be color balanced at 3200K.
69
70. Color Temperature Conversion
– All color cameras are designed to operate at a certain color temperature .
– Ex at 3200K, meaning that the camera will reproduce colors correctly provided that a 3200K illuminant is used.
– Cameras must also provide the ability to shoot under illuminants with color temperatures other than 3200K.
– For this reason, a number of selectable color conversion filters before the prism.
– These filters optically convert the spectrum distribution of the ambient color temperature (illuminant) to that of
3200K, the camera’s operating temperature.
– When only one optical filter wheel is available within the camera, this allows all filters to be Neutral Density types
providing flexible exposure control.
– The cameras also allow color temperature conversion via electronic means.
– The Electronic Color Conversion Filter allows the operator to change the color temperature from 2,000K to 20,000K as
typical.
70
72. Color Temperature Conversion
“Why do we need color conversion filters if we can correct the change of color temperature electrically (white
balance)?".
– White balance electrically adjusts the amplitudes of the red (R) and blue (B) signals to be equally balanced to the
green (G) by use of video amplifiers.
– We must keep in mind that using electrical amplification will result in degradation of signal-to-noise ratio.
– Although it may be possible to balance the camera for all color temperatures using the R/G/B amplifier gains, this is
not practical from a signal-to-noise ratio point of view, especially when large gain up is required.
The color conversion filters reduce the gain adjustments required to achieve correct white balance.
72
73. Variable Color Temperature
The Variable Color Temp. Function allows the operator to change the color temperature from 2,000K to 20,000K
73
74. Preset Matrix Function
– Preset for 3 Matrices can be set.
– The Matrix level can be preset to different lightings.
– The settings can be easily controlled by the control panel.
74
75. White Balance & Color Temperature
The different light source types emit different colors of light (known as color spectrums) and video cameras capture this
difference.
75
76. The video cameras are not adaptive to the different spectral distributions of each light source type.
– In order to obtain the same color reproduction under different light sources, color temperate
variations must be compensated by converting the ambient color temperature to the camera’s
operating color temperature (Optically or Electrically).
– Once the incoming light’s color temperature is converted to the camera’s operating color
temperature (Optically or Electrically), this conversion alone does not complete color balancing of
the camera, therefore more precise color balancing adjustment must be made .
– A second adjustment must be made to precisely match the incoming light’s color temperature to that
of the camera known as “white balance”
76
White Balance
77. White Balance
White balance refers to shooting a pure white object, or a grayscale chart, and adjusting the camera’s video
amplifiers so the Red, Green, and Blue channels all output the same video level.
77
78. 380 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 70 720 760 780
More Precise
Color Balancing
78
White Balance
79. Preset White
Preset White is a white-balance selection used in shooting scenarios
When the white balance cannot be adjusted OR When the color temperature of the shooting environment is already
known (3200K or 5600K for instance).
– This means that by simply choosing the correct color conversion filter, optical or electronic, the approximate white
balance can be achieved.
– It must be noted however, that this method is not as accurate as when taking white balance.
By selecting Preset White, the R/G/B amplifiers used for
white-balance correction are set to their center values.
At center values
79
80. AWB (Auto White Balance)
─ Unlike the human eye, cameras are not adaptive to different color temperatures of different light source types or
environments.
─ The camera must be adjusted each time a different light source is used.
─ This is achieved by adjusting the camera’s white balance to make a ‘white’ object always appear white.
─ The AWB is achieved by framing the camera on a white object – typically a piece of white paper/clothe or
grayscale chart – so it occupies more than 70% of the display.
Macbeth Chart
80
81. Black Balance
─ To ensure Accurate color reproduction throughout all
video levels, it is important that the red, green, and
blue channels are also in correct balance when there
is no incoming light.
─ When there is no incoming light, the camera’s red,
green, and blue outputs represent the “signal floors”
of the red, green, and blue signals, and unless these
signal floors are matched, the color balance of other
signal levels will not match either..
81
82. Black Balance
It is necessary when:
– Using the camera for the first time
– Using the camera after a significant perid out of
use
– Sudden change in temperature
– Without this adjustment, the red, green, and blue color
balance cannot be precisely matched even with
correct white balance adjustments.
82
83. ND (Neutral Density) Filter
83
It reduces light of all wavelengths.
It is used when the subject is too bright to be adjusted by the diaphragm alone.
The ND filters reduce the amount of incoming light to a level where the lens iris can provide correct exposure for even
bright images.
– It is important to note that the use of ND filters does not affect the color temperature of the incoming light – they are
designed so that light intensity is reduced uniformly across the entire spectrum.
– The ND filters can also be used to intentionally control an image’s depth of field to make it more shallow.
– This is because ND filters allow a wider iris opening to be selected, and because depth of field decreases as iris
aperture (opening) increases.
84. ND (Neutral Density) Filter
Strength of an ND filter may be express as:
Transmittance T
Density D
D=-log T
Exposure factor
Exposure factor=1/T
84
85. – A polarizer is used to intercept (stop/catch) light reflected from the surface of water or glass.
– Since light scattered by the atmosphere is partly polarized, polarizer is also effective when shooting subjects
against a blue sky.
– It can suppress the sky and make mountains or other objects stand out.
A polarizer
1- Reduces the total amount of light to about ¼
2- Changes the color balance ,so the white balance must be readjusted.
Polarizer in Camera
85
87. CRT
Control
Grid
Light Input
Input voltage
Output light
Camera
OutputLight
Output voltage
Input light
Input light
Output light
Camera Gamma
CRT Gamma
Legacy system-gamma is about
1.2 to compensate for dark
surround viewing conditions.
ITU-R BT.709
87
89. By lowering the gamma correction value,
you can add extra color depth to image.
on the contrary, setting gamma to a higher
value allows you to shoot images with
lighter hue.
By lowering the camera gamma correction(γc ) value
89
90. CRT
Control
Grid
CRT Gamma
Camera Gamma
Light Input Camera
Output Light
Output voltage
Input light
More contrast in dark picture areas,
(more low-luminance detail).
more noise.
Less contrast in dark picture areas,
(less low-luminance detail).
less noise.
-
+
Black Gamma
90
91. By decreasing black gamma dark areas are reproduced
with more color and darkness .
91
92. CCD image sensors have a dynamic range that is around three to six times
larger than the video signal’s dynamic range.
1) Linear Fashion Mapping:
The picture content most important to us (ordinarily lighted subjects and
human skin tones ) would be reproduced at very low video levels,
making them look too dark on a picture monitor.
Remember that the brightness (luminance) levels need to fit within the 0% to
100% (max. 109%) video signal range.
92
Knee Correction
(max. 109%)
93. Knee Correction
2) Clipping Off or Discarding the Image’s Highlights
This would offer bright reproduction of the main picture content, but with the tradeoff of image highlights
having no contrast and appearing washed out.
93
94. Knee Correction
– Knee Correction offers a solution to both issues, keeping the main content bright while maintaining a certain
level of contrast for highlights.
– The image sensor output is mapped to the video signal so it maintains a proportional relation until a certain
video level. This level is called the knee point.
94
99. – Photo A shows that the scenery outside the window (image highlights) gets overexposed when Knee
Correction is turned off.
– In contrast, by activating the Knee Correction function, both the person inside the car as well as the
scenery outside are clearly reproduced. (Photo B)
99
Knee Correction
103. Image Sensors
– Image sensors have photo-sensors that work in a similar way to our retina’s photosensitive cells, to convert light into
a signal charge.
– However, the charge readout method is quite different!!!!!
103
105. Image Sensor Size
– Image sensor size is measured diagonally across the imager’s photosensitive area, from corner to corner.
– A larger image sensor size generally translates into better image capture.
– This is because a larger photosensitive area can be used for each pixel.
The benefits of larger image sensors can be summarized as follows:
1. Higher sensitivity
2. Less smear
3. Better signal-to-noise characteristics
4. Use of better lens optics
5. Wider dynamic range
105
106. – The term full frame or ff is used by users of digital single-lens reflex cameras (DSLRs) as a shorthand for an image
sensor format which is the same size as 35mm format (36 mm × 24 mm) film.
Image Sensor Size
63.26mm
106
107. CCD and CMOS Image Sensors
107
CCD and CMOS sensors perform the
same steps, but at different locations, and
in a different sequence.
108. Both CCD and CMOS sensors perform all of these steps. However, they differ as to where and in what sequence these steps
occur.
I. Light-to-charge conversion: In the photo-sensitive area of each pixel, light directed from the camera lens is converted
into electrons that collect in a semiconductor "bucket.“
II. Charge accumulation: As more light enters, more electrons come into the bucket.
III. Transfer: The signal must move out of the photosensitive area of the chip, and eventually off the chip itself.
IV. Charge-to-voltage conversion: The accumulated charge must be output as a voltage signal.
V. Amplification: The result of charge-to-voltage conversion is a very weak voltage that must be made strong before it
can be handed off to the camera circuitry.
108
CCD and CMOS Image Sensors
110. CCD Image Sensor
110
– Charge Transfer from Photo Sensor to Vertical CCD
– Like Water Draining from a Dam.
111. CCD Image Sensor
111
– Charge Transfer by CCD in a Bucket-brigade Fashion.
– CCD image sensors get their name from the vertical and horizontal shift registers, which are Charge Coupled
Devices that act as bucket brigades.
112. CCD Image Sensor
112
– Voltage Generated on Surface of Photo Sensor – Like the Rising Water Level of a Bucket
– The downward direction indicates a high voltage. Conversely, the upward direction indicates a high negative
potential, since the charge has a negative electrical value.
113. CMOS Image Sensor
113
– CMOS sensors have an
amplifier at each pixel.
– The charge is first converted
to a voltage and amplified
right at the pixel.
– By placing ADCs so close to
each photo site, these
sensors significantly reduce
the signal's exposure to noise.
114. 114
– Signal Voltage Generated by Amplifier (Like a Floodgate that Controls the Water Level of a Canal)
– The downward direction indicates a high voltage.
– The upward direction indicates a high negative potential, since the charge has a negative electrical value.
CMOS Image Sensor
116. Geometric Distortion in CMOS Sensor
116
– Experienced video shooters often test this phenomenon by rapidly panning the camera back and forth past the legs
of a table.
– A distorted image will show "wobbly legs.“
– Image distortion in a CMOS camera can make a moving car appear to lean backwards.
117. Advantages of CCD:
1. High image quality
2. Low spatial noise (FPN)
3. Typically low dark current
4. High fill factor (relation of the photo
sensitive area to the whole pixel
area) generally by larger pixels
5. Perfect global shutter
– Increased sensitivity
– Good signal quality at low light
6. Modern CCDs with multi tap
technologies
– n times readout speed compared
to single tap sensors
117
Advantages of CMOS:
1. High frame rates, even at high
resolution
2. Faster and more flexible readout (e.g.
several AOIs: Area of Interests)
3. High dynamic range or HDR mode
(Acquisition of contrast-rich and
extremely bright objects)
4. No blooming or smear contrary to CCD
5. Integrated control circuit on the sensor
chip
6. More cost-effective and less power
consumption than comparable CCD
sensors
Image Sensors Comparison
118. Optical Low Pass Filter
– Due to the physical size and alignment of the photo sensors on a CCD imager, when an object with fine
detail (such as a fine striped pattern) is shot, a rainbow-colored pattern known as Moiré may appear
across the image.
– This tends to happen when the image’s spatial frequency exceeds the CCD’s spatial-offset frequency or,
put more simply, when
– The image details are smaller than the spacing between each photo sensor.
– In order to reduce such Moiré patterns from appearing, optical low pass filters are used in CCD cameras.
– An optical low pass filter is placed in front of the CCD prism block to blur image details that may result in
Moiré.
– Since this type of filtering can reduce picture resolution, the characteristics of an optical low pass filter are
determined with special care – to effectively reduce Moiré, but without degrading the camera’s maximum
resolving power.
118
122. CCD Image Chip
Individual Pixels
Volts
Time / H location
Volts
Time / H location
CCD output signal after
integration of charges
Charge levels on Pixels
122
123. Volts
Time / H location Time / H location
Ideal Signal CCD Output Signal
Volts
Distinct edge:
Instantaneous transition from black to white
Blurred edge:
Gradual transition from black to white
123
125. – The video signal output from a CCD unit lacks detail information because the unit’s ability to resolve detail is
limited by the size of the light sensitive pixels.
– An electronic circuit in the camera compensates for the missing detail information in the Video signal.
– It Makes picture edges appear sharper than the native resolution provided by the camera (also called “image
enhancement”).
– This is achieved by overshooting the signal at the picture edges using a spike-shaped signal called the detail
signal.
– The amount of detail correction can usually be adjusted. This is called detail level.
– Increasing the detail level sharpens the picture, while decreasing it softens the picture.
125
Detail Correction/Signal/Level
126. H/V Ratio
Detail correction is applied to both horizontal and vertical picture edges using separate horizontal detail and vertical
detail circuits.
H/V Ratio:
The ratio between the amount of detail applied to the horizontal and vertical picture edges.
– It is important to maintain the balance of the horizontal and vertical detail signals to achieve natural picture
enhancement.
H/V Ratio should thus be checked every time detail signals are adjusted.
126
128. Fine DTL
By expanding the small edge in the low contrast object and compressing the edge component in the high
contract object, the impression for the glare (a bright unpleasant light) of picture with too much edge is reduced
and the natural image can be obtained.
128
Compressing the edge component
in the high contrast object
Expanding the small edge
component in the low contrast object
129. Increasing the detail level will sharpen the image’s picture edges, and decreasing it will soften its texture.
The image will have a softer texture by decreasing the detail level.
129
130. EZ Focus
EZ Focus is a feature that makes manual focusing adjustments much easier.
– When activated, the camera automatically opens the lens Iris to its widest range. This allows the camera operator
to make correct focus adjustments much easier.
– To avoid over-exposure during this mode, the video level is automatically adjusted by activating the electronic
shutter.
– The lens iris will be kept open for several seconds and then return to the same iris opening before EZ Focus was
activated.
130
131. Expanded Focus
The center of the screen on the LCD monitor and viewfinder of the camcorder can be magnified to about twice
the size, making it easier to confirm focus settings during manual focusing.
131
133. Zebra is a feature used to assist manual iris adjustments by displaying a striped pattern (called a ‘zebra pattern’) in the
viewfinder across image highlights above a designated brightness level.
Two types of zebra modes are available:
One to indicate highlights above 100 IRE
Other indicating signal levels between the 70 and 90 IRE range
– The 100 IRE Zebra displays a zebra pattern only across picture areas which exceed 100 IRE, the video level of pure
white in NTSC and PAL. Using this zebra mode, camera operators adjust the lens iris ring until this zebra pattern
appears in the brightest areas of the picture.
– The second zebra mode displays a zebra pattern across highlights between 70-90 IRE, and disappears above the 90
IRE level. This is useful to determine the correct exposure for facial skin tones since properly exposed skin (in the case
of Caucasian skin) usually falls within the 80 IRE areas.
133
Zebra
134. Gain and decibels (dB)
Referring to this table:
A 20 dB signal gain up means the signal level has been boosted by 10 times.
A 6 dB signal drop (= minus 6 dB) means the signal level has fallen to one half.
The most decibel values that need to be remembered are shown in the following table.
134
135. 135
Multi Matrix
– The colors are selected by their Hue (Phase), Saturation, and Width (hue range).
– In conventional color correction or matrix control, control parameters interact with each other.
– The Multi Matrix function allows color adjustments to be applied over a single color range, while keeping other
colors intact.
– The Multi Matrix function divides the color spectrum into 16 areas of adjustment, where the operator can select
the hue and/or saturation of the area to be color modified.
136. 136
─ In Viewfinders due to their small size screens, resolutions can be limited, making precise focus adjustments difficult.
─ This function boosts the viewfinder signal in frequency ranges that correspond to the image’s VERTICAL picture edges.
As a result, sharp images are reproduced on the viewfinder screen, allowing correct focus adjustments.
─ The PEAKING level, which determines the boost level, is adjustable depending on the operator’s preferences.
─ Although higher PEAKING levels offer sharper viewfinder images, when adjusting PEAKING, two factors must be
considered:
I. Raising PEAKING level equally boosts the viewfinder signal’s noise level
II. Too much PEAKING can create excessively bright picture edges along viewfinder characters and icons, such as on-
screen indications including Gain ,ND/CC, Shutter, settings, and markers.
It is therefore important to balance these factors with the required image sharpness on the viewfinder.
Peaking
137. Skin Tone Detail Correction is a function that allows the detail level of a user-specified color to be adjusted (enhanced or
reduced) independently, without affecting the detail signals of other picture areas.
– Skin Tone Detail Correction was originally developed to reduce unwanted image enchantement (detail signals) on
facial imperfections such as wrinkles, smoothening the reproduction of human skin.
– By selecting a specific skin color, the detail signals for that skin color can be individually controlled and suppressed.
– High-end professional video cameras offer a Triple Skin Tone Detail Function, which allows independent detail control
over three user-specified colors. This enhances the flexibility of Detail Correction – one color selection can be used for
reducing the detail level of skin color, and two other selections can be used for either increasing or decreasing the
detail level of two other objects.
137
Skin Tone Detail Correction
Eliminates the DTL edge only for high frequency area of skin tone to have more effect.
138. VF Detail
– As cameras offer higher image resolutions, focus accuracy becomes a more critical issue than ever before.
– In addition to the viewfinder PEAKING function, high-end cameras incorporate a VF DETAIL function, offering a
better choice for facilitating focus adjustments.
– Compared to PEAKING, the VF DETAIL function offers two unique features.
1- The PEAKING sharpens only vertical picture edges, VF DETAIL increases the sharpness of viewfinder images both
vertically and horizontally.
2- The PEAKING is processed within the viewfinder, however VF DETAIL takes place within the camera. This means
that the VF DETAIL function applies sharpness only to video signals shot by the camera, avoiding on-display
characters created in the viewfinder from being overemphasized.
The VF DETAIL mechanism uses a process similar to the camera’s main detail function. However, an exclusive detail
circuit is used to create the detail signals and add them to the video signal sent to the viewfinder display.
138
139. Clear VF DTL
– Clear VF DTL supports fine focusing in critical situation of HDTV shooting
– Enables the camera operator to focus much easier
139
142. Super Resolution Process
– It is not just upscaling from HD signal.
– It includes so called Super Resolution with image enhancement in the Ultra HD band, a new technology to reconstruct
high resolution signals that is not possible in conventional HD processing!
142
143. 4K/HD Simulcast
– Independent image processing for 4K and HD.
– GAMMA Curve, Color and DTL can be adjust together or separately.
143
145. HD cutout function for clear images
145
– In Zoom & Perspective mode, one
portion can be cut out while performing
perspective transformation, according
to the focal length of the lens (The
cutout region can be controlled with a
mouse).
– In simple HD mode two portions can be
cut out at the same time (The cutout
region can be controlled with a
mouse).
146. FWIGSS
FOCUS - Prior to the start of recording,
the camera operator manually sets
the focus following four simple steps.
Switch to manual focus mode Zoom in on subject’s eyes
Adjust focus ring until sharp Zoom out to compose shot
FWIGSS
An acronym used for remembering the six primary device settings on a video camera: focus, white balance, iris, gain,
shutter speed, and sound.
146
147. FWIGSS
White Balance - Prior to the start of recording, the camera operator manually zooms in on a white card held by the subject to
set the white balance.
147
FWIGSS
148. FWIGSS
Iris, Gain, and Shutter Speed - Prior to the start of recording, the camera
operator adjusts the iris, gain, and shutter speed as required or desired until
the shot is properly exposed. The zebra lines are an aid in setting exposure.
148
FWIGSS
149. FWIGSS
Sound - Prior to the start of recording, the camera operator conducts a sound check and adjusts the record levels for
optimal sound reproduction.
149
FWIGSS
151. Light Levels
NIT
− The measure of light output over a given surface area.
1 Nit = 1 Candela per Square Meter
Dynamic Range
− The range of dark to light in an image or system.
− Dynamic range is the ratio between the whitest whites and blackest blacks in an image (10000:0.1)
High Dynamic Range
− Wider range of dark to light.
151
152. In Rec709 100% white (700mV) is reference to 100 Nits
152
Light Levels
153. Light Levels in Stop
𝐻𝑉𝑆 𝐷. 𝑅 𝑤𝑖𝑡ℎ 𝑎 𝑓𝑖𝑥𝑒𝑑 𝑝𝑢𝑝𝑖𝑙𝑖𝑛 𝑖𝑛 𝑆𝑡𝑜𝑝 = log2
10,000 𝑛𝑖𝑡
0.1 𝑛𝑖𝑡
= 16
The dynamic range of the
human eye with a fixed pupil
is normally 105.
104
10−1
𝐻𝑉𝑆 𝐷. 𝑅 𝑖𝑛 𝑆𝑡𝑜𝑝 = log2
100,000 𝑛𝑖𝑡
0.001 𝑛𝑖𝑡
= 26.6
153
154. HDR Two Parts
– There are two parts to High Dynamic Range (HDR)
– Monitor (Display)
– Camera (Acquisition)
– In the Monitor, it is trying to have the range of the material presented to it. Making things brighter with more
resolution.
– In the Camera it is trying to get many more ‘F’ stops, wider dynamic range with the data for that range.
– HDR increases the subjective sharpness of images , perceived color saturation and immersion.
– SDR or LDR is Standard Dynamic Range
154
155. (Inner triangle: HDTV primaries, Outer triangle: UHDTV primaries)
0 .1 .2 .3 .4 .5 .6 .7 .8
0
.1
.2
.3
.4
.5
.6
.7
.8
y
0 .1 .2 .3 .4 .5 .6 .7 .8
0
.1
.2
.3
.4
.5
.6
.7
.8
y
(a) Carnation
x
(b) Geranium and marigold
x
Wide Color Gamut Makes Deeper Colors Available
155
156. BT. 601 and BT.709 Color Spaces
– The maximum (“brightest”) and
minimum (“darkest”) values of the three
components R, G, B define a volume in
that space known as the “color volume”.
– Rec-601 and Rec-709 are basically on
top of each other
– So, we can use the same screen for SD
and HD with out going through
conversion in the Monitor to change the
color space
156
157. BT. 2020 Color Space
– Rec. 2020 color space covers
75.8%, of CIE 1931 while Rec. 709
covers 35.9%.
157
158. Color Gamut Conversion (Gamut Mapping and Inverse Mapping)
158
Wide Color Space (ITU-R Rec. BT.2020)
75.8%, of CIE 1931
Color Space (ITU-R Rec. BT.709)
35.9%, of CIE 1931
A
1
B C
2
D
3
RGB
100% Color Bar
Rec. 709
Rec. 2020
CIE 1931 Color Space
159. (ITU-R Rec. BT.2020)
(ITU-R Rec. BT.709)
Transformation from a Wider Gamut Space to a Smaller One
159
BT.2020 Signal BT.709
– Without any corrections (gamut mapping), the image appear less saturated.
Munsell Chart
A
1
Three Approaches:
I. Clipping the RGB (clipping distortions)
II. Perceptual gamut mapping (more computations and possibly
changing the ‘creative intent’)
III. Leaving the RGB values as they are and let the screen think that they
relate to primaries of ITU-R BT.709.
160. – Without any corrections color saturation will be increased.
Smaller Gamut Space in a Wide Gamut Display
160
Munsell Chart
BT.709 Signal BT.2020
(ITU-R Rec. BT.2020)
(ITU-R Rec. BT.709)
D
3
161. – Opto-Electronic Transfer Function (OETF): Scene light to electrical signal
– Electro-Optical Transfer Function (EOTF): Electrical signal to scene light
Gamma, EOTF, OETF
161
162. – Opto-Electronic Transfer Function (OETF): Scene light to electrical signal
– Electro-Optical Transfer Function (EOTF): Electrical signal to scene light
Gamma, EOTF, OETF
The CRT EOTF is commonly
known as gamma
162
163. – Adjustment or Artistic Intent (Non-Linear Overall Transfer Function)
– System (total) gamma to adjust the final look of displayed images (Actual scene light to display luminance Transfer function)
– The “reference OOTF” compensates for difference in tonal perception between the environment of the camera and that of the display
specification (OOTF varies according to viewing environment and display brightness)
OOTF (Opto-Optical Transfer Function)
OOTF
Same Look
163
164. – On a flat screen display (LCD,
Plasa,..) without OOTF, it appears
as if the black level is elevated a
little.
– To compensate the black level
elevation and to make images
look closer to CRT, a display
gamma = 2.4 has been defined
under BT.1886.
– As a result, OOTF = 1.2
Display EOTF
gamma 2.4
Camera OETF
1/2.2
OOTF = 1.2
OOTF (Overall System Gamma, Artistic Rendering Intent)
Opto-Optical Transfer Function (OOTF)
Non-Linear Overall Transfer Function
164
165. – Perceptual Quantization (PQ) (Optional Metadata)
– Hybrid Log-Gamma (HLG)
OOTF Position
For viewing in the end-user consumer TV, a display mapping should be performed to adjust the reference OOTF on
the basis of mastering peak luminance metadata of professional display
165
OOTF is implemented within the display and is aware of its peak luminance and environment (No metadata)
166. Scene-Referred and Display-Referred
Scene-Referred:
– The HLG signal describes the relative light in the scene
– Every pixel in the image represents the light intensity in the captured scene
– The signal produced by the camera is independent of the display
– The signal is specified by the camera OETF characteristic
Display-Referred:
– The PQ signal describes the absolute output light from the mastering display
– The signal is specified by the display EOTF
166
168. Barten Ramp
Human eye’s sensitivity to contrast in different levels
100
MinimumDetectableContrast(%)
MinimumContrastStep(%)
Luminance (nit)
Contouring
Banding
∆𝐿
𝐿
×100
∆𝐿 & L are Large,Less bits are required,Larger quantize step size∆𝐿 & L are small,More bits are required,Smaller quantize step size
Minimum detectable contrast (%) =
𝐌𝐢𝐧𝐢𝐦𝐮𝐦 𝐝𝐞𝐭𝐞𝐜𝐭𝐚𝐛𝐥𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐢𝐧 𝐥𝐮𝐦𝐢𝐧𝐚𝐧𝐜𝐞
𝐋𝐮𝐦𝐢𝐧𝐮𝐧𝐜𝐞
× 𝟏𝟎𝟎 =
∆𝑳
𝑳
×100
2
L
∆𝑳
𝑳
×100
∆𝑳
𝑳
×100
168
The threshold of visibility for quantization error (Minimum detectable contrast) (banding or
contouring) becomes higher as the image gets darker.
The threshold for perceiving quantization error (banding or contouring) is approximately
constant in the brighter parts and highlights of an image.
169. PQ EOTF
Code words are equally spaced in perceived brightness over this range nits.
BrightnessCodeWords
169
Minimum Detectable Contrast (%) =
𝐌𝐢𝐧𝐢𝐦𝐮𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐚𝐛𝐥𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐢𝐧 𝐋𝐮𝐦𝐢𝐧𝐚𝐧𝐜𝐞
𝐋𝐮𝐦𝐢𝐧𝐮𝐧𝐜𝐞
× 𝟏𝟎𝟎 =
∆𝑳
𝑳
×100
2
L
170. Code Words Utilization by Luminance Range in PQ
– PQ headroom from 5000 to 10,000
nits = 7% of code space
– 100 nits is near the midpoint of the
code range
170
171. 0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1
SignalValue
Linear light
SDR gamma curve
SDR with Knee
HDR HLG
Hybrid Log-Gamma (HLG) HDR-TV
E : The signal for each color component {RS, GS, BS} proportional to scene linear light and scaled by camera exposure,
normalized to the range [0:12].
E′ :The resulting non-linear HLG coded signal {R',G',B'} in the range [0:1].
a = 0.17883277, b = 0.28466892, c = 0.55991073
More CodeWords for DarkArea
112ln
03
OETF
12
1
12
1
EcbEa
EE
EE
171
Less CodeWords for BrightArea
ITU-R Application 2 ,ARIB B67 (Association of Radio Industries and Businesses)
173. Tone Mapping and Inverse Tone Mapping
Tone Mapping (Down-conversion)
Limiting Luminance Range
Inverse Tone Mapping (Up-conversion)
Expanding Luminance Range
173
HDR
BT.2020
SDR Signal
(BT.709 or BT.2020) HDR
HDR Signal
(BT.2020)
SDR Signal
(BT.709 or BT.2020)
SDR
(BT.709 or BT.2020)
HDR Signal
(BT.2020) SDR
174. 174
– Optimized only for the brightest scene in the contents
– This avoids hard clipping of detail in the highlights
– It is not invariant under blind multiple round-trip conversions.
Static Tone Mapping (HDR10)
Static and Dynamic Tone Mapping
200 1500
175. 175
– Optimized for each scene in the contents
– Ex: frame-by-frame, or scene-by-scene basis (Varying the EETF based on statistics of the image).
– This approach could survive multiple round-trip conversions
Dynamic Tone Mapping
Static and Dynamic Tone Mapping
176. Static and Dynamic Metadata in HDR
Static Metadata
– Mastering Display Color Volume (MDCV) Metadata (SMPTE ST2086)
– The chromaticity of the red, green, and blue display primaries
– White point of the mastering display
– Black level and peak luminance level of the mastering display
– Content Light Levels Metadata (The Blu-ray Disc Association and DECE groups):
– MaxCLL (Maximum Content Light Level): Largest individual pixel light value of any video frame in the program
– MaxFALL (Maximum Frame-Average Light Level): Largest average pixel light value of any video frame in the program
(The maximum value of frame-average maxRGB for all frames in the content)
(The frame-average maxRGB : The average luminance of all pixels in each frame)
– They could be generated by the color grading software or other video analysis software.
Dynamic Metadata
– Content-dependent Metadata (SMPTE ST2094 (pending))
– Frame-by-frame or scene-by-scene Color Remapping Information (CRI)
– Variable color transformation along the content timeline.
176
177. Mapping
– During the transition from SDR to HDR production (More SDR Display) or due to content owner preference
– To preserve the “look” of the SDR content on HDR Display
– Display-referred mapping
To preserve the colors and relative tones of SDR on HDR Display
– Scene-referred mapping
To match the colors and lowlights and mid-tones of SDR camera with HDR camera.
177
SDR camera output
(BT.709 or BT.2020)
HDR Signal HDR
BT.2020
Preserved SDR Look
in HDR Program (Ex: 20%)
(Without Expanded
Luminance Range)
HDR Signal
HDR
BT.2020
SDR Content
(BT.709 or BT.2020)
(Without Expanded
Luminance Range)
Preserved SDR Look
in HDR Program (Ex:20%)
178. Backwards Compatibility
– Most of encoder/decoder and TVs are SDR (encoders/decoders replacement !!?? )
– Dolby Vision, Technicolor, Philips and BBC/NHK are all backwards compatible.
– Backwards compatibility is less of an issue in over-the-top (OTT).
HDR Signal
SDR UHDTV
ITU-R BT.709 color space HDR metadata simply is ignored
(Limited compatibility)
178
(Color Signal)
(B & W Display)
179. HLG and PQ Backwards Compatibility with SDR Displays
HLG
BT.2020
SDR
BT.2020 color space
− It has a degree of compatibility.
− Hue changes can be perceptible in bright areas of highly
saturated color or very high code values (Specular)
− Both PQ and HLG provide limited compatibility
HLG/PQ
BT.2020
SDR
BT.709 color space
179
180. Ex: Benefit of 4K Lens for WCG and HDR
– Both HD and 4K lens covers BT.2020.
– Improve the transparency of Blue in 4K lens
– Better S/N ratio.
– 4K lens can cut the flare and reduce black floating even in
a backlit conditions.
– Black floating is more noticeable in HDR.
– Same object and same white level, but black level of
– HD: 21.9% (HD lens reduces dynamic range!)
– Full 4K:11.6%
Same object and
same white level, but
different black level
180
181. HDR & HDMI
HDMI 2.0a supports ST2084 (PQ) and ST2086 (Mastering Display Color Volume Metadata)
HDMI 2.0b followed up on HDMI 2.0a and added support for HLG and the HDR10
The HDMI 2.1 Specification will supersede 2.0b will support dynamic metadata and High Frame Rate
181
185. Global Picture of Sony “SR Live” for Live Productions (FIFA World Cup 2018)
– 8 Cameras Dual output UHD/HDR and HD/SDR
– 11 Cameras Dual output HD/HDR and HD/SDR
– 21 Cameras Single output HD/SDR
– All Replays HD/SDR
Shading of all cameras is done
on the HD/SDR (BT. 709)
185
186. Global Picture of “HLG-Live” for Live Productions
Shading of all cameras is done
on the HD/SDR (BT. 709)
186
187. SD and HD Vectors
709 Color Space 601 Color Space
Vector look is same as each other
187
188. BT.2020 and BT.709 Vectors
709 Color Space 2020 Color Space
Vector look is same as each other
188
189. Standard Definition100% color bar test pattern.
Standard Definition 100% color bar RGB parade
Standard Definition 100% color barYPbPr parade
High Definition 100% color barYPbPr parade
Why small Spikes in the RGB waveform parade? The unequal rise time
between Luma and Color Difference bandwidths and the conversion of
SDI Y’P’bP’r back to R’G’B’ in the waveform display.
189
190. HD 100% color barsYPbPr parade, Rec. 709.
UHD 100% color barsYPbPr parade, Rec. 709.
UHD 100% Color BarsYPbPr parade, Rec. 2020.
Spike transitions is normal because no video filtering is applied to each
link. This allows the quad links to be seamlessly stitched together,
otherwise a thin black line would be seen between the links.
190
191. UHD 100% Split Field Color Bars with both 709 and 2020 color spaces inYPbPr Parade display.
191
192. RGB Paraded waveform display of 100% Color Bar split field test signal with Rec. 709 and Rec. 2020 color spaces.
In some cases the SMPTE 352 VPID may contain information on the colorimetry data that is
used. Often however, this may not be the case and a known test signal such as color bars will be
necessary to assist the user in determining the correct color space.
The user must manually select from the configuration menu between the 709 and 2020
colorspaces.
When the correct colorspace is selected then the traces will be at 0% and 100% (700mv)
levels.
192
193. White and Highlight Level Determination for HDR
Diffuse white (reference white) in video:
Diffuse white is the reflectance of an illuminated white object (white on calibration card).
Since perfectly reflective objects don’t occur in the real world, diffuse white is about 90% reflectance (100% reflectance white card is used either).
The reference level, HDR Reference White, is defined as the nominal signal level of white card.
Highlights (Specular reflections & Emissive objects (self-luminous)) :
The luminances that are higher than reference white are referred to as highlights. In traditional video, the highlights levels were generally set to be no
higher than 1.25×diffuse white level. (in cinema up to 2.7×diffuse white).
- Specular reflections
Specular regions luminance can be over 1000 times higher than the diffuse surface in nit.
- Emissive objects (self-luminous)
Emissive objects and their resulting luminance levels can have magnitudes much higher than the diffuse range in a scene or image. (Sun,
has a luminance s~1.6 billion nits).
A more unique aspect of the emissive is that they can also be of very saturated color (sunsets, magma, neon, lasers, etc.).
Black
White
18% Reflectance
193
194. Nominal signal levels for PQ and HLG production
Reflectance Object or Reference
(Luminance Factor, %)
Nominal Luminance Value, nit
(PQ & HLG)
[Display Peak Luminance, 1000 nit]
Nominal
Signal Level
(%)
PQ
Nominal
Signal Level
(%)
HLG
Grey Card (18%) 26 nit 38 38
Greyscale Chart Max (83%) 162 nit 56 71
Greyscale Chart Max (90%) 179 nit 57 73
Reference Level:
HDR Reference White (100%) also
Diffuse White and Graphics White
203 nit 58 75
─ PQ and HLG production on a display with 1000 nits nominal peak luminance, under controlled studio lighting (Test chart should be
illuminated by forward lights and camera should shoot it from a non-specular direction).
─ The percentages represent signal values that lie between the minimum and maximum non-linear values normalized to the range 0 to 1.
90% Reflectance
18% Reflectance (the closest standard reflectance card to skin tones)
Here, the reference level, HDR Reference White, is defined as the nominal signal level of a 100% reflectance white card. (the signal level that would
result from a 100% Lambertian reflector placed at the center of interest within a scene under controlled lighting, commonly referred to as diffuse white). 194
197. Capturing Camera Log Footage (Spider Cube)
─ Use a suitable grey scale camera chart or Spider
Cube
─ This cube has a hole that produce super black, a
reflective black base, and segments for 18% grey
and 90% reflective white. The ball bearing on the
top produces reflective specular highlights.
─ Setup your test chart within the scene.
─ Adjust the lighting to evenly illuminate the chart.
─ Adjust the camera controls to set the levels
–ISO/Gain, Iris, Shutter, White Balance
Specular
Highlights 18% Grey
90% Reflectance
White
Super
Black
Black
Data color Spider Cube.
197
198. SMPTE 2084 PQ (1K) scale with 100% reflectance white.
NitsLevel (%)
The 90% reflectance white of the signal should be at about 51% level that is equivalent to 100 Nits
The 18% grey will be at 36% level that is equivalent to 20 Nits
10,000Nits is equal to the 100% level of HDR signal Specular
Highlights
18% Grey
90%
Reflectance
White Super Black Black
Data color
Spider
Cube.
The 2% Black point will be at 19% level that is equivalent to 2.2 Nits
Camera operators can use the graticule lines at 2%,
18% or 90% Reflectance to properly setup camera
exposure with a camera test chart of 2% black,
18% gray and 90% white.
198
199. SMPTE 2084 (10K) with 90% reflectance white with graticule scale in terms of reflectance.
NitsLevel (%)
Specular
Highlights
18% Grey
90%
Reflectance
White Super Black Black
Data color
Spider
Cube.
The 90% reflectance white level of the signal should be at about 51% level that is equivalent to 90 Nits
The 18% grey level will be at 36% level that is equivalent to 18 Nits
The 2% Black point level will be at 19% level that is equivalent to 2 Nits
9,000Nits is equal to the 100% level of HDR signal
199
200. SMPTE 2084 (10K) with 90% reflectance white with graticule scale in terms of Code Values.
Level (Hex)CodeValue (Decimal)
200
201. SMPTE 2084 (10K) with 90% reflectance white with graticule scale in terms of STOPS
StopLevel (%)
201
202. 10K PQ with a 1000 nits Limit, Full range
Waveform in 10K PQ Full range with theVideo at 1K grade.
If you use the full 10K curve and set your video grading to 1000 Nits you will have about the top
25% of the waveform screen not being used.
We have implemented both Narrow (SMPTE) SDI levels and Full SDI levels.
Waveform setting on 10K PQ Full range: On the waveform you see 4d as 0 nits and 1019d as 10,000 nits in Full.
202
203. HDR 1k Grade SMPTE Levels
Normal reflectiveWhites are around 100Nits
Peek is going to 1000Nits no more.
HDR has the blacks stretch and theWhites are compressed.
203
204. HDR Reflectance View
The normal reflective whites are around 100Nits, which is at 90% Reflectance (709 100 IRE)
18% grey will be at 36% level that is equivalent to 18 Nits
2% Black will be at 19% level that is equivalent to 2 Nits
1000 Nits shows up at 100% Reflectance
204
205. Stop View
(Relative to 20 nits)
Stop
1000 Nits
The normal reflectiveWhites are around 100Nits, which is at +2.3 Stops (709 100 IRE)
0 Stop is shown as the 18% Grey point (=20 nits).
2% Black point at -3.1 Stops.
𝑆𝑡𝑜𝑝 𝑉𝑎𝑙𝑢𝑒 𝑓𝑜𝑟 1000 𝑛𝑖𝑡𝑠 = log2
1000 𝑛𝑖𝑡
20 𝑛𝑖𝑡
= 5.5
205
206. HDR 2K Grade SMPTE Levels.
NormalWhites are just around 100Nits just a little higher.
Max white is at 2000Nits
HDR has the blacks stretch and theWhites are compressed.
206
207. HDR 1K Grade Full Levels
Black (0) is at 4h
White is around 100Nits
Highlights are going up to 1000Nits
Waveform setting on 10K PQ Full range: On the waveform you see 4d as 0 nits and 1019d as 10,000 nits in Full.
207
208. Rec 709 Video on the HDR Graticule
Whites are going to 100%.
The black are all down at the bottom of the waveform.
The whites are stretched to 100%
208
211. HDR Heat-map tool
─ 7 simultaneous and programmable color overlay bands
─ Individual upper & lower overlay threshold controls
─ User presets for SDR & HDR modes
─ Selectable background grey /color
─ Identify shadows, mid-tones or specular highlights
211
212. Capturing a Camera Log Image
Gamma
0% Black
10-bit Code-Value
%
18% Grey 10-bit Code-Value
(20nits illumination)
%
90% Reflectance
10-bitCode Value
%
S-Log1 90 3 394 37.7 636 65
S-Log2 90 3 347 32.3 582 59
S-Log3 95 3.5 420 40.6 598 61
Log C (ARRI) 134 3.5 400 38.4 569 58
C-Log (Canon) 128 7.3 351 32.8 614 63
ACES (Proxy) ND ND 426 41.3 524 55
BT.709 64 0 423 41.0 940 100
─ Today’s video cameras are able to capture a wide dynamic range of 14-16 Stops depending on the camera.
─ In order to record this information a log curve is used by each camera manufacturer to be able to store this wide dynamic range
effectively with 12- 16 bits or resolution as a Camera RAW file.
─ Each curve has defined Black, 18% Grey and 90% reflectance white levels.
212
213. S-Log2 Waveform to nits
540 or 1000 Nits
Max Highlights
Monitor dependent
(Display with 540 or
1000 nits)
100 Nits (59%)
NormalWhite
20 Nits (32.3%)
18% Grey
213
214. Spider Cube S-Log2 as Shot from the Camera in Log
DigitalValues Stop values
214
215. Spider Cube S-Log2 as Shot from the Camera in Log
Showing S-Log2 in normal 709 type screens
215
217. Camera (Scene) Referenced BT.709 to PQ LUT Conversion
─ SDR and HDR displays DO NOT match.
─ Blacks are stretched in the BT1886 Display but not the PQ Display (matches scene)
Camera-Side Conversion
BT.709 to PQ
2084 HDR 0% 2 % 18% 90% 100%
BT.709 100nits 0 9 41 95 100
HDR 1000nits 0 37 58 75 76
HDR 2000nits 0 31 51 68 68
HDR 5000nit 0 24 42 58 59
9 41 95 100
217
218. Specification of color bar test pattern for high dynamic range TV systems
BT.2111-07
(40%)
(75%)
(0%)(75%)(0%)
(0%)
(75%)
(40%)
(75% colour bars)
(100% colour bars)
(–2%) (+2%) (+4%)
BT. 709 colour bars
Ramp (–7% - 109%)
Stair (–7%, 0%, 10%, 20%, ..., 90%, 100%, 109%HLG)
Recommendation ITU-R BT.2111-0
(12/2017)
Specification of colour bar test pattern for
high dynamic range television systems
BT Series
Broadcasting service
(television)
218
219. Color Correcting your 4K Content
Image without full dynamic range. Blacks are lifted (above 0) and whites aren't at 100% (or 700 mv).
219
224. Misbalanced Chip Chart.
Chip Chart is only made up of black, white and gray chips,
so the entire trace should be very close to the center.
Balanced Chip Chart
224
225. A fairly balanced image on an RGB Parade waveform monitor, but the image contains a lot of green grass
225