Name
Abstract | ||
|---|---|---|
The current standard image-compression approaches rely on fairly simple predictions, using either block-or wavelet-based methods. While many more sophisticated texture-modeling approaches have been proposed, most do not provide a significant improvement in compression rate over the current standards at a workable encoding complexity level. We re-examine this area, using example-based texture prediction. We find that we can provide consistent and significant improvements over JPEG, reducing the bit rate by more than 20% for many PSNR levels. These improvements require consideration of the differences between residual energy and prediction/residual compressibility when selecting a texture prediction, as well as careful control of the computational complexity in encoding. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5652402 | 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING |
Keywords | Field | DocType |
Image compression, Texture analysis | Computer vision,Residual,Data compression ratio,Pattern recognition,Computer science,Transform coding,JPEG,Artificial intelligence,Image compression,Computational complexity theory,Encoding (memory),Wavelet | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyu Cui | 1 | 222 | 11.83 |
| Saurabh Mathur | 2 | 117 | 6.76 |
| Michele Covell | 3 | 706 | 78.42 |
| Vivek Kwatra | 4 | 1578 | 93.15 |
| Mei Han | 5 | 952 | 57.87 |