Chapter 5 : IMAGE COMPRESSION – LOSSLESS COMPRESSION -

1. Chapter 5 : IMAGE COMPRESSION – LOSSLESS COMPRESSION - Asmawati @ Nur Hidayah Bte Jusoh (IT 01481) Azmah Bte Abdullah Sani (IT 01494) Dina Meliwana Bte Asjhad Zaenie (IT 02373) Ummi Susanti Bte Rafiei (IT 02145)

2. Contents: 5.1. Overview of Image Compression 5.2. Lossless Compression Methods 5.3. Application Of Lossless Compression 5.4. Recent Research Of Lossless Compression.

3. Overview Of Image Compression Introduction • Process of reducing or compressing size and image data • files but still retaining important information. • compressed file is used to reconstruct image. • relationship between compressed and uncompressed file is • denoted as the compression ratio : Uncompressed file size SIZEU Compression Ratio = = Compressed file size SIZEC

4. Fidelity Criteria Fidelity Criteria • Criterion used to determine important information to be • retained when compressing files. • Divided into 2 classes : • i. Objective fidelity criteria ii.Subjective fidelity criteria What are the objective fidelity criteria & subjective fidelity criteria??

5. Fidelity Criteria • Objective fidelity criteria measures amount of error in • decompressed image. The smaller the value of error metrics, • the better the compressed image. • Errors are measured using : • ROOT- MEAN-SQUARE ERROR • ROOT-MEAN-SQUARE SIGNAL-TO-NOISE RATIO(SNR) • PEAK SIGNAL-TO-NOISE RATIO • Subjective testing is performed by creating a database of • image to be tested.

6. Fidelity Criteria • Images will be tested by having all test subjective to evaluate • them according to a predefined scoring criterion. • The results are then analyzed statistically using average & • standard deviations as metrics. • 3 category of tests: • 1. IMPAIRMENT TEST • assess images in terms of how bad they are • 2. QUALITY TEST • assess images in terms of how good they are • 3. COMPARISON TEST • evaluate images on a side-by side basis

7. Compression System Model • Compressor • Preprocessing - Data reduction : Reduce image by using gray level and/or spatial quantization or any image enhancement process - Mapping : Map image data into other mathematical space ( easier to compress data ) • Encoding - Quantization : Takes potentially continuous from mapping stage and put in discrete form - Coding : Maps discrete data onto a code

8. Compression System Model 2)Decompressor • Decoding - Reverse coding of compressed file by mapping to original • Inverse Mapping - Reverse mapping process • Postprocessing - Enhance the look of final image

9. Lossless Compression Method Introduction : Lossless compression methods guarantees that the decompressed image is absolutely identical to the image before compression. Lossless Compression Methods: 1) Huffman Coding 2) Run – Length Coding 3) Lempel – Ziv – Welch Coding 4) Arithmetic Coding

10. Lossless Compression Methods<< HUFFMAN CODING >> HUFFMAN CODING - developed by D. A. Huffman - based on the fact that in an input stream certain tokens occur more often than others. - generating codes that closest to entrophy.

11. Lossless Compresion Methods<<Step By Step >> Step By Step… • Find the gray – level probabilities for the image by finding the histogram. • Order the input probabilities (histogram magnitudes ) from smallest to largest. • Combine the smallest two. (add the two smallest) • GOTO step 2, until only two probabilities are left. • By working backward along the tree, generate code by alternating assignment of 0 and 1.

12. Lossless Compression Methods<< RUN – LENGTH CODING >> RUN – LENGTH CODING • used when there is a tendency for long runs of repeated • digitized gray levels to occur. • the Run-length coding (RLC) works by counting the number • of adjacent pixels with the same gray-level value.

13. Lossless Compression Methods<< Step By Step >> Step By Step… • Define the required parameters horizontally or vertically. • Define a convention for the first RLC number in a row (represent a run of 0’s or 1’s). • Extend basic RLC using Bit-plane RLC – works by applying basic RLC to each bit plane independently. • The compression results is then improved using preprocessing to reduce the number of gray level. • Then , the reduce image data mapped to a Gray Code,

14. Lossless Compression Methods<< Example >> Horizontal Run Length Coding Using the convention of 0’s as the first value. No of rows 8 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 ROW 1 4, 1, 3 ROW 2 1, 2, 2, 2, 2 ROW 3 1, 6, 1 ROW 4 1, 8 ROW 5 1, 1, 1, 1, 1, 1, 1, 1 ROW 6 4, 3, 1 ROW 7 1, 1, 1, 2, 3 ROW 8

15. Lossless Compresion Methods<<Lempel-Ziv-Welch Coding>> • LEMPEL-ZIV-WELCH CODING (LZW) • - works by coding strings of data • for images, the strings correspond to sequence of pixel value • - a string table that contains the strings and their corresponding value is created • - the table is updated as the file is read, with new codes being inserted whenever a new string is encountered

16. Lossless Compresion Methods<<Lempel-Ziv-Welch Coding>> - if the table already contains the string, the corresponding code for that string is put into the compressed file - uses code words with more bits than the original data - the table consists of the original 2n entries (corresponding to the original n-bit data) and allows another 2n-k - 2n entries for string codes

17. Lossless Compression Method<<Arithmetic Coding>> • ARITHMETIC CODING • transforms input data into a single floating point between • 0 and 1 • the image must be divided into small subimages to be encoded • works by successively subdividing the interval between 0 and 1 • impractical to use alone

18. Lossless Compresion Methods<<Arithmetic Coding>> Step By Step… • Find the probability for each pixel • Divide the initial subinterval based on the distribution • The first pixel value is coded by extracting the subinterval corresponding to it, and subdivide again based on the same relative distribution • Repeat step (3) for each pixel value until a final interval is determined • Any value within this subinterval can be used to represent the sequence of gray-level values

19. Application Of Lossless Compression • QuickTime ‘Animation’ Codec • - suited for storage of two-dimensional animation and computer- • generated video content. • PICTools Medical Compression Toolkit’s High Speed • Lossless JPEG by Pegasus. • - uses in the field of cardiology and ultrasound • imaging.

20. Application Of Lossless Compression • Voice E-mail 4.0 (Voice E-mail’s Lossless Audio • Compression) • - uses the latest in digital audio compression technology to • compress messages before transferring them through • CompuServe, America Online, Microsoft Network, and the • Internet.

21. Recent Study @ Research Of Lossless Compression… • PPG Lossless Image Compression Project by UCLA • Develop/implement a lossless compression/decompression scheme for use • with the UCLA PACS Image Archive satisfying the following • Key Technical Requirements: • 1. Relatively high speeds of compression/decompression. 2. Flexible enough to work with image data from all modalities. 3. Robust/high-reliability code and support environment (e.g. compilers). 4. Longevity, i.e. be able to restore original images after 20 years.