Vector quantization and signal compression
Vector quantization and signal compression
Digital Image Processing
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A new approach to the design of a finite-state vector quantizer (FSVQ) is proposed. FSVQ essentially exploits correlations between adjacent blocks for efficient coding. Previous FSVQ design schemes had ad-hoc features in defining states and resource allocation using equal number of bits for state codebooks regardless of their probabilities of occurrence in a given source. We propose a FSVQ design approach which improves the compression performance by merging states and using variable state-codebook sizes. Another undesirable feature of the FSVQ is a derailment problem which degrades the performance in many practical applications. We propose a structurally constrained state-codebook design approach that eliminates the derailment problem. The performance of the proposed algorithm outperforms previously known FSVQ methods. Further development of the algorithm utilizing mean-removed VQ is described which gives less block artifact even though PSNR is a little bit inferior.