Some experiments on vector quantization using neural nets

Some applications of neural network algorithms for the design of vector quantizer codebooks are presented. Vector quantization has been applied to the problem of displaying natural images with a reduced set of colors (color palette) and to the interframe coding of image sequences. Self-organizing feature maps and competitive learning (CL) algorithms have been used for the codebook design and their results are compared with those obtained using a classical LBG (Linde, Buzo, Gray) algorithm. The best results are obtained using a CL algorithm with a new initialization strategy. With this technique the codebook design is very fast, and some experiments have been carried out in order to introduce adaptive vector quantization in the interframe coder. Frame by frame the codebooks used to quantize the most important information are updated and then the more active parts of the motion-compensated luminance differences are quantized with these new codebooks. A considerable rise of the coder performance was obtained.<
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