Image compression and it’s security1

Image Compression
And It’s Security
M.Tech (CSE)
GNIT
Submitted by : Reyad Hossain
Submitted to : Mr .Moloy Dhar
9/16/2015M.Tech (CSE)..GNIT 1

Outline
1.About Compression Technique.
2.Fundamental Of Image Compression.
a) General Image Storage System.
b) Color Specification.
3.Operation Steps.
a) Encoding.
b) Decoding.
4. RCBP and Quantization.
5.Image Compression Algorithms.
6. Application Of Image Compression.
7. Advantage and Disadvantage
8. Conclusion.
9. References.

1)Compression:
product grows increasingly fast, contributing to insufficient bandwidth of network and
storage of memory device. Therefore, the theory of data compression becomes more and
more significant for reducing the data redundancy to save more hardware space and
transmission bandwidth. In computer science and information theory, data compression or
source coding is the process of encoding information using fewer bits or other
information-bearing units than an unencoded representation. Compression is useful
because it helps reduce the consumption of expensive resources such as hard disk space or
transmission bandwidth.
In recent years, the development and demand of multimedia

2) Fundamental Of Image Compression:
application of data compression that encodes the original image with few bits. The
objective of image compression is to reduce the redundancy of the image and to store or
transmit data in an efficient storage system. The main goal of such system is to reduce
the storage quantity as much as possible, and the decoded image displayed in the monitor
can be similar to the original image as much as can be. The essence of each block will be
introduced in the following sections.
Image compression is and

a)General Image Storage System:
Object
R-G-B
coordinate
Transform to
Y-Cb-Cr
coordinate
Downsample
Chrominance
Encoder
Decoder
R-G-B
coordinate
Transform to
R-G-B
coordinate
Upsample
Chrominance
Monitor
C
Camera
C
V
HDD
Performance
RMSE
PSNR
Figure: General Image Storage System

b) Color Specification:
-Luminance
Received brightness of the light, which is proportional to the total energy in the
visible band.
-Chrominance
Describe the perceived color tone of a light, which depends on the wavelength
composition of light
Chrominance is in turn characterized by two attributes
-Hue
Specify the color tone, which depends on the peak wavelength of the light
-Saturation
Describe how pure the color is, which depends on the spread or bandwidth of
the light spectrum

YUV Color Space:
-In many applications, it is desirable to describe a color in terms of its luminance and
chrominance content separately, to enable more efficient processing and transmission of
color signals
-One such coordinate is the YUV color space
-Y is the components of luminance
-Cb and Cr are the components of chrominance
-The values in the YUV coordinate are related to the values in the RGB coordinate by
0.299 0.587 0.114 0
0.169 0.334 0.500 128
0.500 0.419 0.081 128
Y R
Cb G
Cr B
      
      
         
             

3) Operation Steps:
Image Compression Coding
-What is the so-called image compression coding?
-To store the image into bit-stream as compact as possible and to display the decoded
image in the monitor as exact as possible
-Flow of compression
-The image file is converted into a series of binary data, which is called the bit-stream
-The decoder receives the encoded bit-stream and decodes it to reconstruct the image
-The total data quantity of the bit-stream is less than the total data quantity of the
original image
Encoder 0101100111... Decoder
Original Image Decoded Image
Bitstream

Flow Of Image Compression
-Measure to evaluate the performance of image compression
-Root Mean square error:
-Peak signal to noise ratio:
-Compression Ratio:
Where n1 is the data rate of original image and n2 is that of the encoded bit-
stream
-The flow of encoding
-Reduce the correlation between pixels
-Quantization
-Source Coding
Reduce the
correlation
between
pixels
Quantization
Source
Coding
Bitstream
Original
Image

a) Encoding:
Mapper: transforms input data in a way that facilitates reduction of interpixel
redundancies.

Quantizer: reduces the accuracy of the mapper’s output in accordance with some
pre-established fidelity criteria.

Symbol encoder: assigns the shortest code to the most frequently occurring
output values.

2) Decoding:
Inverse steps are performed.
Note that quantization is irreversible in general.

4) RCBP:
Reduce the Correlation between Pixels
Why an image can be compressed? The reason is that the correlation between one pixel and its neighbour
pixels is very high, or we can say that the values of one pixel and its adjacent pixels are very similar. Once
the correlation between the pixels is reduced, we can take advantage of the statistical characteristics and the
variable length coding theory to reduce the storage quantity. This is the most important part of the image
compression algorithm; there are a lot of relevant processing methods being proposed. The best-known
methods are as follows:
Predictive Coding: Predictive Coding such as DPCM (Differential Pulse Code Modulation) is a lossless
coding method, which means that the decoded image and the original image have the same value for every
corresponding element.
Orthogonal Transform: Karhunen-Loeve Transform (KLT) and Discrete Cosine Transform (DCT) are
the two most well-known orthogonal transforms. The DCT-based image compression standard such as
JPEG is a lossy coding method that will result in some loss of details and unrecoverable distortion.
Subband Coding: Subband Coding such as Discrete Wavelet Transform (DWT) is also a lossy coding
method. The objective of subband coding is to divide the spectrum of one image into the lowpass and the
highpass components. JPEG 2000 is a 2-dimension DWT based image compression standard.

4) Quantization:
The objective of quantization is to reduce the precision and to achieve higher compression
ratio. For instance, the original image uses 8 bits to store one element for every pixel; if we
use less bits such as 6 bits to save the information of the image, then the storage quantity
will be reduced, and the image can be compressed. The shortcoming of quantization is that
it is a lossy operation, which will result into loss of precision and unrecoverable distortion.
The image compression standards such as JPEG and JPEG 2000 have their own
quantization methods.

5) Compression Algorithms:
JPEG – Joint Picture Expert Group
Fig. shows the Encoder and Decoder model of JPEG. We will introduce the operation
and fundamental theory of each block in the following sections.
Image
R
G
B
YVU color
coordinate
Chrominance
Downsampling
(4:2:2 or 4:2:0)
8 X 8
FDCT
Quantizer
Quantization
Table
zigzag
Differential
Coding
Huffman
Encoding
Huffman
Encoding
Bit-stream
Fig: JPEG Encoding

Decoded
Image
R
G
B
Chrominance
Upsampling
(4:2:2 or 4:2:0)
8 X 8
IDCT
YVU color
coordinate
Dequantizer
Quantization
Table
De-zigzag
De-DC
coding
Huffman
Decoding
Huffman
Decoding
Bit-stream
Fig: JPEG Decoding

Quantization In JPEG:
Quantization is the step where we actually throw away data.
Luminance and Chrominance Quantization Table
-lower numbers in the upper left direction
-large numbers in the lower right direction
-The performance is close to the optimal condition
Quantization
( , )
( , )
( , )
Quantization
F u v
F u v round
Q u v
 
  
 
Dequantization ( , ) ( , ) ( , )deQ QuantizationF u v F u v Q u v 
16 11 10 16 24 40 51 61
12 12 14 19 26 58 60 55
14 13 16 24 40 57 69 56
14 17 22 29 51 87 80 62
18 22 37 56 68 109 103 77
24 35 55 64 81 104 113 92
49 64 78 87 103 121 120 101
72 92 95 98 112 100 103 99
YQ
 
 
 
 
 
 
 
 
 
 
  
 
17 18 24 47 99 99 99 99
18 21 26 66 99 99 99 99
24 26 56 99 99 99 99 99
47 66 99 99 99 99 99 99
99 99 99 99 99 99 99 99
99 99 99 99 99 99 99 99
99 99 99 99 99 99 99 99
99 99 99 99 99 99 99 99
CQ
 
 
 
 
 
 
 
 
 
 
  
 

JPEG 2000:
Image
R
G
B
Forward
Component
Transform
2D DWT Quantization EBCOT
Context
Modeling
Arithmetic
Coding
Rate-
Distortion
Control
Tier-2
Tier-1
JPEG 2000
Bit-stream
Fig: JPEG 2000 Encoding

EBCOT
Decoder
JPEG 2000
Bit-stream Inverse
Component
Transform
2D IDWTDequantization Decoded
Image
R
G
B
Fig: JPEG 2000 Decoding

6) Application Of Image Compression:
-The many benefits of image compression include less required storage space, quicker sending and receiving of images,
and less time lost on image viewing and loading. But where and how is image compression used today?
-Just as image compression has increased the efficiency of sharing and viewing personal images, it offers the same
benefits to just about every industry in existence. Early evidence of image compression suggests that this technique was,
in the beginning, most commonly used in the printing, data storage, and telecommunications industries. Today however,
the digital form of image compression is also being put to work in industries such as fax transmission, satellite remote
sensing, and high definition television, to name but a few.
-Just as image compression has increased the efficiency of sharing and viewing personal images, it offers the same
benefits to just about every industry in existence. Early evidence of image compression suggests that this technique was,
in the beginning, most commonly used in the printing, data storage, and telecommunications industries. Today however,
the digital form of image compression is also being put to work in industries such as fax transmission, satellite remote
sensing, and high definition television, to name but a few.
-In the security industry, image compression can greatly increase the efficiency of recording, processing and storage.
However, in this application it is imperative to determine whether one compression standard will benefit all areas. For
example, in a video networking or closed-circuit television application, several images at different frame rates may be
required. Time is also a consideration, as different areas may need to be recorded for various lengths of time. Image
resolution and quality also become considerations, as does network bandwidth, and the overall security of the system.

7) Advantages and Disadvantages:
Size Reduction
File size reduction remains the single most significant benefit of image compression. Depending on what file type you're working with, you
can continue to compress the image until it's at your desired size. This means the image takes up less space on the hard drive and retains the
same physical size, unless you edit the image's physical size in an image editor. This file size reduction works wonderfully for the Internet,
allowing webmasters to create image-rich sites without using much bandwidth or storage space.
Slow Devices
Some electronic devices, such as computers or cameras, may load large, uncompressed images slowly. CD drives, for example, can only read
data at a specific rate and can't display large images in real time. Also, for some webhosts that transfer data slowly, compressed images
remain necessary for a fully functional website. Other forms of storage mediums, such as hard drives, will also have difficulty loading
uncompressed files quickly. Image compression allows for the faster loading of data on slower devices.
Degradation
When you compress an image, sometimes you will get image degradation, meaning the quality of the image has declined. If saving a GIF or
PNG file, the data remains even though the quality of the image has declined. If you need to show a high-resolution image to someone,
large or small, you will find image compression as a disadvantage.
Data Loss
With some common file types, such as JPEG, when an image shrinks in size the compression program will discard some of the photo's
data permanently. To compress these images, you need to ensure you had an uncompressed backup before starting. Otherwise, you will lose
the high quality of the original uncompressed image permanently.

8) Conclusion:
Overall, JPEG’s compression process is an extremely useful type of image compression.
It is ideal for storing photographs since visually unimportant information can be easily
discarded (of which photographs have quite a bit) and has become one of the most
common file types used. This paper has demonstrated the ability of the JPEG
compression process to effectively retain visual quality in an image while drastically
reducing the file size through the implementation of the Discrete Cosine
Transform,quantization,reordering, and Huffman coding.

9) References:
[1] Austin, David. Image Compression: Seeing What’s Not There. American
Mathematical
Society. 2009. Accessed 24 Aug. 2009.
[2] “Discrete Cosine Transform.” Image Processing Toolbox. Mathworks. Accessed 4
Sept. 2009.
[3] Gonzalez,Rafael C. and Richard E. Woods and Steven L. Eddis.Digital Image
Processing
Using MATLAB. Gatesmark Publishing. 2009. Pages 283-323.
[4] Harris, Greg A. and Darrel Hankerson. “Transform Methods and Image
Compression.”
Web. January 1st, 1999. Accessed September 2, 2009.
[5] Holloway, Catherine. JPEG Image Compression: Transformation, Quantization,
and Encoding. April 2008. Accessed 22 Sept. 2009.
[6] “Image Types.” Mathworks Helpdesk. Accessed 22 Sept. 2009.

Thank You For Listening….

Image compression and it’s security1

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