Name
Abstract | ||
|---|---|---|
In this paper, we propose a context-based inverse quantization and show its application in wavelet image compression. The proposed method breaks the traditional one-to-one mapping of the quantization index to reconstruction value in inverse quantization while maps an index to several different reconstruction values according to the corresponding contexts of the index. By accurate context modeling, this method can reduce the quantization distortion significantly. Since the quantization indices used for encoding is not changed, this method does not increase the encoding bit rates except the negligible overhead. ©2009 IEEE. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISCAS.2009.5117823 | ISCAS |
Keywords | Field | DocType |
probability distribution,wavelet transforms,information science,indexation,quantization,data mining,image compression,image reconstruction,data compression,context model,context modeling,entropy,indexes | Iterative reconstruction,Pattern recognition,Computer science,Context model,Vector quantization,Trellis quantization,Artificial intelligence,Quantization (image processing),Quantization (signal processing),Data compression,Wavelet transform | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4244-3828-0 | 0 | 0.34 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jicheng An | 1 | 36 | 6.51 |
| Zixing Cai | 2 | 1525 | 66.96 |
| Quqing Chen | 3 | 74 | 7.58 |
| Zhibo Chen | 4 | 306 | 44.72 |
| Jun Teng | 5 | 8 | 1.14 |