Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11042-015-2463-2 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Vector quantization, Principal component analysis, Huffman code, Compression efficiency | Pattern recognition,Computer science,Vector quantization,Huffman coding,Artificial intelligence,Code word,Principal component analysis,Lossless compression,Encoding (memory),Fold (higher-order function),Codebook | Journal |
Volume | Issue | ISSN |
75 | 6 | 1573-7721 |
Citations | PageRank | References |
2 | 0.37 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chin Chen Chang | 1 | 7849 | 725.95 |
| Tzu-Chuen Lu | 2 | 374 | 33.17 |
| Gwoboa Horng | 3 | 463 | 35.36 |
| Ying-Hsuan Huang | 4 | 167 | 9.08 |