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A Fast Encoding Algorithm for Image Vector Quantization Based on Prior Test of Multiple Features  

Ryu Chul-hyung (국방과학연구소 1체계-1부-2팀)
Ra Sung-woong (충남대학교 정보통신공학부 영상통신 연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.30, no.12C, 2005 , pp. 1231-1238 More about this Journal
Abstract
This paper presents a new fast encoding algorithm for image vector quantization that incorporates the partial distances of multiple features with a multidimensional look-up table (LUT). Although the methods which were proposed earlier use the multiple features, they handles the multiple features step by step in terms of searching order and calculating process. On the other hand, the proposed algorithm utilizes these features simultaneously with the LUT. This paper completely describes how to build the LUT with considering the boundary effect for feasible memory cost and how to terminate the current search by utilizing partial distances of the LUT Simulation results confirm the effectiveness of the proposed algorithm. When the codebook size is 256, the computational complexity of the proposed algorithm can be reduced by up to the $70\%$ of the operations required by the recently proposed alternatives such as the ordered Hadamard transform partial distance search (OHTPDS), the modified $L_2-norm$ pyramid ($M-L_2NP$), etc. With feasible preprocessing time and memory cost, the proposed algorithm reduces the computational complexity to below the $2.2\%$ of those required for the exhaustive full search (EFS) algorithm while preserving the same encoding quality as that of the EFS algorithm.
Keywords
vector quantization; full search equivalent; fast encoding; multiple features;
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