Index compression for vector quantization using principal index-pattern coding algorithm

Yung-Chih Liu; Gwo-Her Lee; Jan-Ray Liao; Li-Pin Chi; Jinshiuh Taur

doi:10.1117/1.JEI.23.4.043015

31 July 2014 Index compression for vector quantization using principal index-pattern coding algorithm

Yung-Chih Liu, Gwo-Her Lee, Jan-Ray Liao, Li-Pin Chi, Jinshiuh Taur

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 4, 043015 (July 2014). https://doi.org/10.1117/1.JEI.23.4.043015

Abstract

This paper presents an efficient lossless compression algorithm, the coding tree assignment scheme with principal index-pattern coding algorithm (CTAS-PIPCA), to encode image vector quantization (VQ). The coding model is designed on the basis of the schemes proposed in the previous works to further improve the coding performance of coding tree assignment scheme with improved search-order coding algorithm (CTAS-ISOC) by PIPCA. The PIPCA technique exploits the correlation of neighboring index pairs not in the original vector-quantized index map but in the principal index-pattern table which is generated from the two-dimensional histogram of index patterns in the training stage. The CTAS-PIPCA method is evaluated via extensive experiments. The searching matched index in the principal index-pattern table results in lower time complexity than CTAS-ISOC. The results also show that the proposed technique apparently reduces the bit rate as compared to the conventional VQ and other existing popular lossless index coding schemes, such as SOC and CTAS-ISOC.

Citation Download Citation

Yung-Chih Liu, Gwo-Her Lee, Jan-Ray Liao, Li-Pin Chi, and Jinshiuh Taur "Index compression for vector quantization using principal index-pattern coding algorithm," Journal of Electronic Imaging 23(4), 043015 (31 July 2014). https://doi.org/10.1117/1.JEI.23.4.043015

Published: 31 July 2014

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