A fast evolutionary algorithm in codebook design
Abdolali Momenai, Siamak Talebi
Abstract
Abdolali Momenai, Siamak Talebi
Abstract
This paper presents a fast algorithm to find an optimal subset codebook from a super codebook in a way that the RMS error between the new codebook and the training set in vector quantization becomes minimum. To have a fast algorithm, a genetic based algorithm is used that uses 2 evolutions, one in designing the whole sub-codebook and the other in finding each individual codeword of the sub-codebook. This optimal codebook can be efficiently used in the real time compression of the images with PSNR improvement of about 1.1 - 8.2db in blocks of the image.
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This paper presents a fast algorithm to find an optimal subset codebook from a super codebook in a way that the RMS error between the new codebook and the training set in vector quantization becomes minimum. To have a fast algorithm, a genetic based algorithm is used that uses 2 evolutions, one in designing the whole sub-codebook and the other in finding each individual codeword of the sub-codebook. This optimal codebook can be efficiently used in the real time compression of the images with PSNR improvement of about 1.1 - 8.2db in blocks of the image.
Key concepts: Codebook, Linde–Buzo–Gray algorithm, Vector quantization, Computer science, Algorithm, Code word, Genetic algorithm, Image compression