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A New Dynamic Finite-State Vector Quantization Algorithm for Image Compression

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. VQ for image coding. VQ which exploits the correlation among neighboring blocks Predictive VQ

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A New Dynamic Finite-State Vector Quantization Algorithm for Image Compression

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  1. A New Dynamic Finite-State Vector QuantizationAlgorithm 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) X2 X1

  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.)

  7. Simulation Results

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

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

  10. 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.

  11. 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.

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