Vector quantization and signal compression
Vector quantization and signal compression
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
Lossless compression of VQ index with search-order coding
IEEE Transactions on Image Processing
Inverse error-diffusion using classified vector quantization
IEEE Transactions on Image Processing
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
Fast-searching algorithm for vector quantization using projection and triangular inequality
IEEE Transactions on Image Processing
Side match and overlap match vector quantizers for images
IEEE Transactions on Image Processing
Predictive classified vector quantization
IEEE Transactions on Image Processing
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In this paper, a fast search algorithm for mean-removed vector quantization is proposed. Two inequalities are used to reduce distortion computations. Our algorithm makes use of a mean-removed vector's features (edge and texture strengths) to reject many unlikely codewords and it has the same image quality as the full search method. Experimental results show that our algorithm is much better than the full search method in terms of computing time and the number of distortion calculations. Comparing with the full search method, our method can effectively reduce the computing time by 60.2-94.2% and the number of distortion computations by 78.6-97.9% for the codebook sizes of 64-2048. As far as we know, our method is the first of its kind to reduce the encoding time for mean-removed vector quantization.