Image compression_btech

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

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

Image CompressionImage Compression

EXAMPLE OF Psychovisal RedundancyEXAMPLE OF Psychovisal Redundancy

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

IGS QUANTIZATION EXAMPLEIGS QUANTIZATION EXAMPLE

Objective Fidelity criteria

Fidelity criteria

Subjective Fidelity criteriaSubjective Fidelity criteria

General Compression System ModelGeneral Compression System Model

Chapter 8Image Compression

ERROR FREE COMPRESSION

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

Another Method

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

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

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

39 39 126 126

LZW Coding

Consider 4x4 , 8 Bit image::

Dictionary Location

255 255

256 --

511 --

512 Word Dictionary

LZW CodingLZW Coding

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

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.

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

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.

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

In gray code:127 = 01000000128 = 11000000

Bit Plane CodingBit Plane Coding

Bit Plane Coding Bit Plane Coding

Lossless Predictive CodingLossless Predictive Coding

•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.

nnn ffe ˆ

nnn fef ˆDecompression:

iinin froundf

ˆ Most general form :

Most Simple form

Lossy Predictive CodingLossy Predictive Coding

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.

Assignment

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

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.

Arithmetic CodingArithmetic Coding

0.0592

0.0624

0.0688

0.06368

0.06496

End of Unit5 : Image Compression

Image compression_btech

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