Name
Abstract | ||
|---|---|---|
Most texture compression schemes are not able to accurately represent the object edges, since the number of color candidates usually are not sufficient to reconstruct a smooth block. We propose a new hierarchical palette approach for the texture compression. The colors are chosen from palettes and sub-palettes in quad-tree without increasing bit rates. The experimental results show significant improvements over most existing texture compression methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BigMM.2016.50 | 2016 IEEE Second International Conference on Multimedia Big Data (BigMM) |
Keywords | Field | DocType |
texture compression,hierarchical palette,color candidates,quadtree | Computer vision,Texture compression,Computer science,Image texture,Adaptive Scalable Texture Compression,Color Cell Compression,S3 Texture Compression,Artificial intelligence,Texture atlas,Texture filtering,Image compression | Conference |
ISBN | Citations | PageRank |
978-1-5090-2180-2 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-Hao Yang | 1 | 0 | 1.35 |
| Chih-Hung Kuo | 2 | 86 | 14.77 |
| Yu-Kuan Lin | 3 | 0 | 0.34 |