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– Finite-state VQ (FSVQ)– Dynamic FSVQ– Address VQ– Index search VQ

Vector Quantization (VQ)

X1

X2

DFSVQ

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.

Proposed DFSVQ (cont.)

Simulation Results

VQ

0.563 bpp,

31.10 dB

DFSVQ-N

(0.430 bpp. 31.06 dB),

Original

SMVQ

(0.412 bpp, 31.10 dB),

PDFSVQ

0.246 bpp, 31.07 dB

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.

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.