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.