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Fast codeword search algorithm for ECVQ using hyperplane decision rule

Vector quantization is the process of encoding vector data as an index to a dictionary or codebook of representative vectors. One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through the codebook. Entropy-constrained vector quantization (ECVQ) codebook design based on empirical data involves an expensive training,phase in which Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we describe a new method allowing significant acceleration in codebook design process. This method has feature of using a suitable hyperplane to partition the codebook and image data. Experimental results are presented on image block data. These results show that our method performs better than previously known methods.