Abstract
In this paper, we propose a novel compression method that can efficiently compress a vector quantization (VQ) index table. Before compressing the VQ index table, the method sorts all of the codewords in the VQ codebook by principal component analysis (PCA), assuring that each codeword has extreme similarity to its adjacent codewords. Afterwards, in the VQ index table, the difference between the current compressed VQ index and one of its adjacent VQ indices is calculated as the compression code, and the indicators generated by the Huffman code method are used to identify the encoding length of each difference. In other words, each VQ index is replaced by one indicator and the difference, which are variable-length codes. The experimental results showed that the compression efficiency of the proposed method is superior to that of the other lossless data compression methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
