1. A New Dynamic Finite-State Vector Quantization Algorithm for Image Compression Jyi-Chang Tsai, Chaur-Heh Hsieh, and Te-Cheng Hsu IEEE TRANSACTIONS ON IMAGE PROCESSINIG, NOVEMBER 2000

2. VQ for image coding VQ which exploits the correlation among neighboring blocks –Predictive VQ –Finite-state VQ (FSVQ) –Dynamic FSVQ –Address VQ –Index search VQ

3. Vector Quantization (VQ) X1 X2

4. DFSVQ

5. Proposed DFSVQ Search the best block in predefined search area which contains previously encoded data. The current input block can be represented by the best block, dynamic codebook or super-codebook. The search for the the best block from the the search area is equivalent to expanding the code- vector space. Thus the picture is superior to the basic VQ with full search method.

6. Proposed DFSVQ (cont.)

10. Simulation Results

14. VQ 0.563 bpp, 31.10 dB DFSVQ-N (0.430 bpp. 31.06 dB), Original

15. SMVQ (0.412 bpp, 31.10 dB), PDFSVQ 0.246 bpp, 31.07 dB

17. Conclusions For each input block, the PDFSVQ first searches the best block. Then, the current block is encoded by the best block, dynamic codebook or super-codebook, depending on the coding distortion. The PDFSVQ exploits the global correlation of image blocks rather than local correlation in conventional memory VQs.

18. Conclusions (cont.) The PDFSVQ expands the codebook space without extra overhead information bits; thus, it achieves better rate-dis-tortion performance and visual quality than conventional DFSVQs.