{'fraction_non_alphanumeric': 0.011479591836734694, 'fraction_numerical': 0.0, 'mean_word_length': 5.28, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Compressing Vector Quantization Index Table Using Side Match State Codebook

In the memoryless vector quantization scheme, each image block is independently encoded as a corresponding index and then an index table will be generated. In this paper, we apply the side match concept and propose a new scheme, which can further compress the index table without introducing extra encoding distortion. Our scheme exploits the characteristic that the blocks of images are highly correlated to their neighboring indices in the compression process. To increase the compression, the side match concept is applied to dynamically generate a state codebook for each encoding block. Simulation results show that our schemes are superior to SOC and traditional memoryless VQ on the compression rate.