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

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.