Smooth Side-Match Classified Vector Quantizer with Variable Block Size

1. Smooth Side-Match Classified Vector Quantizer with Variable Block Size IEEE Transaction on image processing, VOL. 10, NO. 5, MAY 2001 Department of Applied Mathematics National Chung Hsing University Shiueng Bien Yang and Lin Yu Tseng

2. Outline • Introduction • Basic Algorithm • Smooth Side-Match Method with Variable Block Size • Genetic Clustering algorithm • Experimental Results • Conclusion

3. Introduction • The evolution of SMVQ • SMVQ • SMVQ with CVQ • SSM-CVQ • Feature of SSM-CVQ • Variable block size • Smooth side-match method • Genetic clustering algorithm is applied on codebooks generation

4. Basic algorithm SMVQ

5. Basic algorithmSMVQ with CVQ (encoder)

6. Basic algorithmSMVQ with CVQ (decoder)

7. Smooth Side-Match Method with Variable Block Size • Variable Block Size • Image Compression with Variable Block Size Segmentation • Quadtree is used to address blocks of different sizes • Smooth side-match method • Diagonal basic blocks • Smooth side-match distortion

8. Image Compression with Variable Block Size Segmentation

9. Quadtree is used to address blocks of different sizes

10. Block size and codebooks • Blocks of sizes of 16x16 and 8x8 and 4x4 with low variance are low-detail blocks • Use three master codebooks • 4x4 • 8x8 • 16x16 • Blocks of size of 4x4 with high variance are high-detail blocks • Use CLUSTERING algorithm, we have q classes and q master codebooks for each class • Total : 3 + q master codebooks

11. Diagonal basic blocks • Diagonal blocks are encoded first. • In the experiments, the number of the basic blocks required is approximately 25% to 28% of that of the conventional SMVQ.

12. Smooth side-match distortion (1) • The encoded is divided into two parts • Upper triangular region • Lower triangular region • Problem of SMVQ • Different, dif(e, f) is defined as • dif(e, f) = (gray level of e) – (gray level of f)

13. Smooth side-match distortion (2) • Upper triangular region

14. Smooth side-match distortion (3) • Lower triangular region

15. Genetic Clustering Algorithm (1) • First Stage Use nearest neighbor (NN) algorithm to reduce the computation time and space in the second stage. (1) (2) (3) (4) Let the connected components be denoted by

16. Genetic Clustering Algorithm (2) • Second Stage Use genetic algorithm to find an appropriate number of clusters. Initialization Step chromosome (string): numbers of 1’s in the strings almost uniformly distributes within [1,m]

17. Gene Chromosome Individual Genetic Clustering Algorithm Data Representation

18. N strings is randomly generated. Nindividuals Population Size=N

19. Genetic Clustering Algorithm Evolution Processes • Self Reproduction • Crossover • Mutation

20. Genetic Clustering Algorithm Fitness Function

21. Genetic Clustering Algorithm Self Reproduction if k=

22. Genetic Clustering Algorithm Crossover Set Probability of crossover Position q Randomly generate If Position=q

23. Genetic Clustering Algorithm Mutation Set Probability of mutation Randomly generate If

24. Experimental results • High-detailed Blocks：why 28 edge-classifiers • Outside image: Lena & F-16 • The PSNRs of the coding for Lena • SSM-CVQ outperforms the others in both the PSNR & the bit rate

25. High-detailed Blocks：why 28 edge-classifiers

26. JPEG Lena 32.01 0.2681 SMVQ with CVQ 30.44 0.2704 Outside image: Lena & F-16

27. The PSNRs of the coding for Lena CLUSTERING is best !

28. SSM-CVQ outperforms the others in both the PSNR & the bit rate

29. Conclusion • The CLUSTERING clusters the appropriate number of clusters. • Low-detail blocks could reduce bit rates • High-detail blocks and smooth side-match distortion could increase quality