Paper
22 October 1993 Finite-state residual vector quantizer for image coding
Steve Shih-Yu Huang, Jia-Shung Wang
Author Affiliations +
Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.158004
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States
Abstract
Finite state vector quantization (FSVQ) has been proven during recent years to be a high quality and low bit rate coding scheme. A FSVQ has achieved the efficiency of a small codebook (the state codebook) VQ while maintaining the quality of a large codebook (the master codebook) VQ. However, the large master codebook has become a primary limitation of FSVQ if the implementation is carefully taken into account. A large amount of memory would be required in storing the master codebook and also much effort would be spent in maintaining the state codebook if the master codebook became too large. This problem could be partially solved by the mean/residual technique (MRVQ). That is, the block means and the residual vectors would be separately coded. A new hybrid coding scheme called the finite state residual vector quantization (FSRVQ) is proposed in this paper for the sake of utilizing both advantage in FSVQ and MRVQ. The codewords in FSRVQ were designed by removing the block means so as to reduce the codebook size. The block means were predicted by the neighboring blocks to reduce the bit rate. Additionally, the predicted means were added to the residual vectors so that the state codebooks could be generated entirely. The performance of FSRVQ was indicated from the experimental results to be better than that of both ordinary FSVQ and RMVQ uniformly.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steve Shih-Yu Huang and Jia-Shung Wang "Finite-state residual vector quantizer for image coding", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.158004
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KEYWORDS
Distortion

Computer programming

Quantization

Image compression

Image quality

Image processing

Signal to noise ratio

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