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An improved fast encoding algorithm for vector quantization using 2-pixel-merging sum pyramid data structure
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AI
DOI：10.1016/j.patrec.2003.12.009.png)
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摘要
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Vector quantization (VQ) is an asymmetric coding method and the winner search in encoding process is extremely time-consuming. This property of VQ constrains its practical applications very much. Based on the sum pyramid data structure of a vector, a fast encoding algorithm with PSNR equivalent to full search is proposed in this paper to improve the results that have been given in [Pattern Recognition Lett. 22(3/4) (2001) 373]. Three major modifications are made. Because sum can be computed accurately using integer operation, mismatched winners due to roundoff error of taking the mean can be overcome firstly by using sum instead of the mean. Secondly, since sum is computed by 2-pixel-merging other than 4-pixel-merging way, more levels can be constructed in a sum pyramid so that the rejection to current codeword can be realized easier. Finally, codebook is off-line rearranged by sorted real sums directly, and the lower and upper bound for promising codeword class are updated dynamically to narrow search scope further once a better-matched codeword has been found during winner search process. For the same four 512 x 512, 256-level standard test images, experimental results show that the proposed algorithm outperforms the method used in [Pattern Recognition Lett. 22(3/4) (2001) 373] in the meaning of total equivalent distance computation being reduced obviously. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
fast encoding
vector quantization
2-pixel-merging
sum pyramid
bound update