Lossless Compression Schemes of Vector Quantization Indices Using State Codebook

In a memoryless vector quantization system, each image block is independently encoded as a corresponding index and then an index table will be generated. In this paper, we propose two novel schemes to compress the index table without introducing extra encoding distortion. Both our schemes exploit the characteristic that the blocks of images are highly correlated to find the same or similar index from the neighboring indices in the compression process. To increase the compression, the principal component analysis (PCA) technique is also employed to sort the codewords in the codebook for minimizing the difference of neighboring indices. In addition, our second scheme excludes the repetitive indices from the search path to further decrease the bit rate. Simulation results show that our schemes are superior to SOC and traditional memoryless VQ on the compression rate.