Digital Image Processing-9

7/28/2019 Digital Image Processing-9

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Color Image Processing

Image Compression


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Topics Color Image Processing

Color Models

RGB

YIQ HSI

Image Compression Lossy and lossless methods

Run length coding Entropy coding

Arithmetic coding

Jpeg coding


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Color Image Processing: Motivation Color is an important feature for detecting and

identifying objects

Human face detection

License plate detection

Content based image retrieval systems

Image segmentation can be performed base on color

values Tracking moving objects in video (surveillance )

Etc.


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Color Spectrum


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Visible Wavelength


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Absorption of light by human eye


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Red, Green, Blue Color Cube


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RGB Color Cube


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YIQ Color ModelYIQ is the color space used by the NTSC color TV

system

The Y component represents the luma information,and is the only component used by black-and-whitetelevision receivers

I and Q represent the chrominance information

For example, applying a histogram equalization to acolor image is done by Y component only


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YIQ Color Model Conversion


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HSI (or HSV) Color Model


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Conversion from RGB to HSI


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Enhancement using HSI Model


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Color Space Conversions in

MATLAB (1) To convert from RGB to HSV use rgb2hsv For HSV to RGB conversion use hsv2rgb

E.g.

I = imread(test.bmp);

H = rgb2hsv(I);


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MATLAB (2) To convert from RGB color space into YIQ color space

use rgb2ntsc

RGB = imread(sample.png');

YIQ = rgb2ntsc(RGB);

ntsc2rgb convert from YIQ to RGB

Images can be displayed in RGB space only


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MATLAB (3) I component in YIQ (the first component) is

equivalent to a gray scale conversion. However, youmay also use rgb2gray either.

I=imread(test.bmp);

YIQ = rgb2ntsc(I);

imshow( YIQ(:,:,1))


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Image Compression


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Image Compression Importance of Image Compression

Typical image resolution/depth

1024 x 1024 x 24

Typical image size in bytes: 3 MB

Typical video rate/resolution/depth

30 fps, 720576 , 24 bits

Typical video size for 1 minute: More than 2 GB


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Data Redundancy Redundancy in information theory is the number of

bits used to transmit a message minus the number ofbits of actual information in the message.

Informally, it is the amount of wasted "space" used totransmit certain data


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Data Compression Data compression or source coding is the process of

encoding information using fewer bits (or otherinformation-bearing units) than an un-encodedrepresentation would use through use of specificencoding schemes.


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Compression Methods Lossless Methods

Lossy Methods


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Run Length Coding In this method runs of data (that is, sequences in which the

same data value occurs in many consecutive data elements)are stored as a single data value and count.

Useful in relatively simple graphic images such as icons,line drawings, and animations.

Example,

WWWWWWWWWWWWBWWWWWWWWWW

WWBBBWWWWWWWWWWWWWWWWWW

WWWWWWBWWWWWWWWWWWWWW

is stored as : 12W1B12W3B24W1B14W


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Entropy Coding Entropy coding is a lossless coding method.

Entropy coding creates variable length codeword. Thelength of each codeword is approximately proportionalto the negative logarithm of its probability.

Huffman Coding

Huffman coding is an entropy encoding algorithm

used for lossless data compression. The term Entropy Coding refers to the use of a

variable-length code table for encoding a sourcesymbol


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Example of Variable Length Coding


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Huffman Coding Example


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Huffman Coding Example


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Arithmetic Coding The output from an arithmetic coding process is a single

number less than 1 and greater than or equal to 0

This single number can be uniquely decoded to createthe exact stream of symbols that went into itsconstruction.


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Example: a1a2a3a3a4


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Example


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Lossy CompressionsA lossy compression is a data compression method

which discards (loses) some of the data, in order toachieve a high compression rate, with the result that

decompressing the data yields content that is differentfrom the original one.


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JPEG Standard Steps followed in JPEG

Color space transformation

Down-sampling

Block splitting

Discrete cosine transform

Quantization

Run length and Entropy coding


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Color space transformation First, the image should be converted from RGB into

YCbCr color space

The compression is more efficient as the brightnessinformation, which is more important to the eventualperceptual quality of the image, is confined to a singlechannel, more closely representing the human visualsystem.


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Down Sampling Due to the densities of color- and brightness sensitive

receptors in the human eye, humans can seeconsiderably more fine detail in the brightness of an

image (the Y component) than in the color of an image(the Cb and Cr components).

Down-sample Cb and Cr components only 4:2:0


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Splitting the Frame into BlocksAfter sub-sampling, each channel must be split into

88 blocks of pixels.


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Discrete cosine transform


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Discrete Cosine Transform


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Result of DCT


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Quantization Quantization is done by simply dividing each

component in the frequency domain by a constant forthat component, and then rounding to the nearest

integer


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Sample Quantization Table


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Quantization Results


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Zig-Zag Scanning and Run Length

Coding


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Zig-Zag Scanning


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Variable length codesVariable length codes are generated for the code words

of the zig-zag scan result.

Trailing zeros are truncated by inserting an EOBsymbol after the last non-zero value.


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Typical compression ratios The compression ration depends on the quantization

factors used.

Typical ratio is 4:1


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Questions?

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