Name
Abstract | ||
|---|---|---|
Adaptive vector quantization (AVQ) is a flexible adaptive single pass image compression method, with fast decompression time, for which practical compression achieved compares favorably with existing methods such as the JPEG standard and traditional trained VQ. In this paper we analyze its asymptotic performance for the lossless case and present variations to limit the effect of transmission errors on the decoding image. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.ins.2004.03.020 | Inf. Sci. |
Keywords | Field | DocType |
jpeg standard,image compression method,flexible adaptive single pass,fast decompression time,asymptotic performance,channel error,present variation,image coding,practical compression,decoding image,adaptive vector quantization,lossless case,image compression | Vector quantization,Artificial intelligence,Quantization (image processing),Adaptive coding,Computer vision,Algorithm,JPEG,Quantization (signal processing),Data compression,Machine learning,Mathematics,Image compression,Lossless compression | Journal |
Volume | Issue | ISSN |
171 | 1-3 | 0020-0255 |
Citations | PageRank | References |
9 | 0.68 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Rizzo | 1 | 86 | 11.21 |
| James A. Storer | 2 | 931 | 156.06 |
| Bruno Carpentieri | 3 | 256 | 32.41 |