A Coding Algorithm Using PCA-Based Correlation Vector Quantization

By the improved coding algorithm proposed in the paper, before encoding, we project all the code words on the principal axis which is determined by principal component analysis (PCA), and a sorted codebook is obtained according to the ascending order of the projection values. During the encoding stage, we use the high correlation of the adjacent image blocks and the projection value of the input vector on the principal axis to determine the searching range. The experimental results show that PCA and correlation vector quantization can accelerate the encoding speed and increase the compression rate compared with the full search vector quantization, although the encoding quality degrades a little.