A fuzzy classified vector quantizer for image coding

Vector quantization of images raises problems of complexity in codebook search and subjective quality of images. The family of image vector quantization algorithms proposed in this paper addresses both of those problems. The fuzzy classified vector quantizer (FCVQ) is based on fuzzy set theory and consists basically in a method of extracting a subcodebook from the original codebook, biased by the features of the block to be coded. The incidence of each feature on the blocks is represented by a fuzzy set that captures its (possibly subjective) nature. Unlike the classified vector quantizer (CVQ), in the FCVQ a specific subcodebook is extracted for each block to be coded, allowing a better adaptation to the block. The CVQ may be regarded as a special case of the FCVQ. In order to explore the possible correlation between blocks, an estimator for the degree of incidence of features on the block to be coded is included. The estimate is based on previously coded blocks and is obtained by maximizing a possibility; a distribution that intends to represent the subjective knowledge on the feature's possibility of occurrence conditioned to the coded blocks is used. Some examples of the application of a FCVQ coder to two test images are presented. A slight improvement on the subjective quality of the coded images is obtained, together with a significant reduction on the codebook search complexity and, when applying the estimator, a reduction of the bit rate.<
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