Hierarchy-oriented searching algorithms using alternative duplicate codewords for vector quantization mechanism

In this paper, we will show those algorithms that speed up the search of a closest codeword over a given codebook. The mechanism is based on a multilevel concept constructed as a hierarchical organization. For a given codebook with size N (existing N codewords), there are only @?log"[email protected]? levels that need to be constructed in order to find the closest codeword, where k is a parameter selection to optimize the comparisons in the course of the searching. Theoretically, the comparisons required to find the closest codeword are on average [email protected]?log"[email protected]?, which is substantially faster than that of a full search job. Besides, in order to approach the perfect match with the exact one among the codewords, a duplicate mechanism is also applied to our algorithm so that the lowest possible distortion is achieved. As is observed from the results of the experiments, the estimation of comparisons in our execution, without codeword duplicate, is on average about [email protected]?log"[email protected]?/N times the full search method. It is worth noting that the larger the size of the codebook, the more the speed increases. In particular, there are two classifications, non-duplicate and duplicate method which are associated with the codewords distribution in the dominated set, which were experimented on some tables. The duplicate is manipulated by the training set containing six popular image data, to demonstrate the efficiency of our scheme. Also, the time requirement to find the closest codeword is effectively reduced, especially in the case of a large sized codebook. Therefore, our scheme offers a novel exploration and achieves a faster performance in the closest codeword searching for vector quantization of image data.

[1]  Chaur-Heh Hsieh,et al.  Fast search algorithms for vector quantization of images using multiple triangle inequalities and wavelet transform , 2000, IEEE Trans. Image Process..

[2]  Robert M. Gray,et al.  An Improvement of the Minimum Distortion Encoding Algorithm for Vector Quantization , 1985, IEEE Trans. Commun..

[3]  Chang-Hsing Lee,et al.  A fast search algorithm for vector quantization using mean pyramids of codewords , 1995, IEEE Trans. Commun..

[4]  Chin-Chen Chang,et al.  An efficient computation of Euclidean distances using approximated look-up table , 2000, IEEE Trans. Circuits Syst. Video Technol..

[5]  Peter R. Cappello,et al.  Systolic architectures for vector quantization , 1988, IEEE Trans. Acoust. Speech Signal Process..

[6]  Kuldip K. Paliwal,et al.  Fast K-dimensional tree algorithms for nearest neighbor search with application to vector quantization encoding , 1992, IEEE Trans. Signal Process..

[7]  G. S. Stiles,et al.  Fast full search equivalent encoding algorithms for image compression using vector quantization , 1992, IEEE Trans. Image Process..

[8]  R. Gray,et al.  Speech coding based upon vector quantization , 1980, ICASSP.

[9]  Nasser M. Nasrabadi,et al.  An efficient Euclidean distance computation for vector quantization using a truncated look-up table , 1995, IEEE Trans. Circuits Syst. Video Technol..

[10]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[11]  M. Reza Soleymani,et al.  A fast MMSE encoding technique for vector quantization , 1989, IEEE Trans. Commun..

[12]  P. A. Ramamoorthy,et al.  Bit-serial VLSI implementation of vector quantizer for real-time image coding , 1989 .

[13]  Ezzatollah Salari,et al.  A fast vector quantization encoding method for image compression , 1995, IEEE Trans. Circuits Syst. Video Technol..

[14]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[15]  Viktor K. Prasanna,et al.  Modular VLSI architectures for real-time full-search-based vector quantization , 1993, IEEE Trans. Circuits Syst. Video Technol..