Distortion sensitive competitive learning for vector quantizer design

We propose the distortion sensitive competitive learning (DSCL) algorithm for codebook design in image vector quantization. The algorithm is based on the equidistortion principle for an asymptotically optimal vector quantizer after Gersho (1979) and from Ueda and Nakano (1994). The DSCL is simple and efficient in that a single weight vector update is performed per training vector, and the processing speed of the DSCL in a sequential or multiprocessor environment can further be improved by applying a modified partial distance elimination (MPDE) method. Simulations indicate that the DSCL outperforms some previously proposed neural algorithms, including the “neural-gas” from Martinetz et al. (1993) and the DEFCL from Butler and Jiang (1996). In combining with the MPDE, the DSCL is faster than the “neural-gas” up to a factor of 45 times on a sequential machine, and yet arrives at better codebooks with the same number of iterations