Fast Image Vector Quantization with Self-Organizing Maps

C. Foucher, D. Le. Guennec, and G. Vaucher (France)


image compression, vector quantization, coding time, self-organizing map.


Vector quantization (VQ) is an efficient technique for lossy image compression, but it often suffers from computing complexity. We propose to reduce coding time in image vector quantization by using natural inter-block correlations and a topologically ordered codebook. Such a codebook is obtained by a self-organizing map (SOM), a neural unsupervised learning algorithm. During coding, when block content changes smoothly, the search for a code vector is limited to the previously used code vector’s neighbourhood instead of the entire codebook (exhaustive search). In both exhaustive and non exhaustive coding modes, the Partial Distance Search (PDS) is used to find the nearest neighbour. The algorithm was tested and we obtained a coding time reduction of up to 54% comparing to PDS and 85% com paring to full search.

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