Image compression_btech

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Digital Image Processing, 2nd ed. www.imageprocessingbook.com 002 R. C. Gonzalez & R. E. Woods Unit 5: Image Compression •Fundamentals •Redundancies •Fidelity criteria •Image Compression Models •Error free compression •Lossy Compression •Image compression Standards :Binary Image & continuous tone still image compression standards •Video compression standards

Transcript of Image compression_btech

Page 1: Image compression_btech

Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Unit 5: Image Compression

•Fundamentals

•Redundancies

•Fidelity criteria

•Image Compression Models

•Error free compression

•Lossy Compression

•Image compression Standards :Binary Image &

continuous tone still image compression standards

•Video compression standards

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Image CompressionImage Compression

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Image CompressionImage Compression

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Image CompressionImage Compression

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Image CompressionImage Compression

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

EXAMPLE OF Psychovisal RedundancyEXAMPLE OF Psychovisal Redundancy

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

IGS quantization procedure

1. Set initial SUM = 0000 0000

2. if most significant 4 bits of current pixel A = 1111 new_SUM = A + 0000 else new_SUM = A + least significant 4 bits of old SUM

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

IGS QUANTIZATION EXAMPLEIGS QUANTIZATION EXAMPLE

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Objective Fidelity criteria

Fidelity criteria

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Subjective Fidelity criteriaSubjective Fidelity criteria

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

General Compression System ModelGeneral Compression System Model

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Chapter 8Image Compression

Chapter 8Image Compression

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

ERROR FREE COMPRESSION

• Huffman Coding• LZW Coding• Bit Plane Coding•Constant Area Coding• Run length coding•Lossless Predictive Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Chapter 8Image Compression

Chapter 8Image Compression

Another Method

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Assignment :Q1 Generate the IGS code for following gray level values of pixels.

100,120,130,170,160,110

Q2 Generate Huffman code for certain message, The frequency of occurrence of elements given below

A=20,B=30,C=10,D=0,E=10,F=20,G=10

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

LZW (Lempel-Ziv-Welch) coding, assigns fixed-length code words to variable length sequences of source symbols, but requires no a priori knowledge of the probability of the source symbols.

The nth extension of a source can be coded with fewer average bits per symbol than the original source.

LZW is used in:•Tagged Image file format (TIFF)•Graphic interchange format (GIF)Portable document format (PDF)LZW was formulated in 1984

LZW Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

The Algorithm:•A codebook or “dictionary” containing the source symbols is constructed.

•For 8-bit monochrome images, the first 256 words of the dictionary are assigned to the gray levels 0-255

•Remaining part of the dictionary is filled with sequences of the gray levels

LZW Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

39 39 126 126

39 39 126 126

39 39 126 126

39 39 126 126

LZW Coding

Consider 4x4 , 8 Bit image::

Dictionary Location

Entry

0 0

1 1

: :

255 255

256 --

: :

511 --

512 Word Dictionary

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

LZW CodingLZW Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Important features of LZW:•The dictionary is created while the data are being encoded. So encoding can be done “on the fly”

•The dictionary need not be transmitted. Dictionary can be built up at receiving end “on the fly”

•If the dictionary “overflows” then we have to reinitialize the dictionary and add a bit to each one of the code words.

•Choosing a large dictionary size avoids overflow, but spoils compressions

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Decoding LZW:Let the bit stream received be: 39 39 126 126 256 258 260 259 257

126In LZW, the dictionary which was used for encoding need not be

sent with the image. A separate dictionary is built by the decoder, on the “fly”, as it reads the received code words.

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

An m-bit gray scale image can be converted into m binary images by bit-plane slicing. These individual images are then encoded using run-length coding.

However, a small difference in the gray level of adjacent pixels can cause a disruption of the run of zeroes or ones.

Eg: Let us say one pixel has a gray level of 127 and the next pixel has a gray level of 128.In binary: 127 = 01111111& 128 = 10000000Therefore a small change in gray level has decreased the run-lengths in all the bit-planes!

Bit Plane CodingBit Plane Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

GRAY CODE•Gray coded images are free of this problem which affects images which are in binary format.• In gray code the representation of adjacent gray levels will differ only in one bit (unlike binary format where all the bits can change.

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Let gm-1…….g1g0 represent the gray code representation of a binary number.Then:

11

1 20

mm

iii

ag

miaag

In gray code:127 = 01000000128 = 11000000

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Bit Plane CodingBit Plane Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Bit Plane Coding Bit Plane Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Chapter 8Image Compression

Chapter 8Image Compression

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Lossless Predictive CodingLossless Predictive Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

•Based on eliminating the interpixel redundancy in an image•We extract and code only the new information in each pixel•New information is defined as the difference between the actual (fn) and the predicted value, of that pixel.

nf̂

nnn ffe ˆ

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

nnn fef ˆDecompression:

m

iinin froundf

1

ˆ Most general form :

Most Simple form

nn ff

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Lossy Predictive CodingLossy Predictive Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Lossy compression•Lossless compression usually gives a maximum compression of 3:1 (for monochrome images)•Lossy compression can give compression upto 100:1 (for recognizable monochrome images) 50:1 for virtually indistinguishable images•The popular JPEG (Joint Photographic Experts Group) format uses lossy transform-based compression.

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Assignment

Q1> Write short note on :•Image compression Standards :Binary Image & continuous tone still image compression standards•Video compression standards•JPEG 2000

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods 35

Arithmetic Coding

• Arithmetic coding bypasses the idea of replacing an input symbol with a specific code. It replaces a stream of input symbols with a single floating-point output number.

• Arithmetic coding is especially useful when dealing with sources with small alphabets, such as binary sources, and alphabets with highly skewed probabilities.

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

Arithmetic CodingArithmetic Coding

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

0.2

0.4

0.8

0.04

0.08

0.16

0.048

0.056

0.072

0.0592

0.0624

0.0688

0.06368

0.06496

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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com

© 2002 R. C. Gonzalez & R. E. Woods

End of Unit5 : Image Compression