Name
Abstract | ||
|---|---|---|
Transform domain down-scaling (TDDS) is traditionally implemented by dropping most of high-frequency components of the transformed block. Applying it to image compression can improve the compression efficiency by saving considerable bit-cost. Due to losing some necessary high-frequency information, the resulted image compressed by using the traditional TDDS-based coding often suffers a serious quality degradation. In this paper, we propose a compression-constrained TDDS and perform it on each N × N block to produce an N/2 × N/2 coefficient block for the compression. Our proposed TDDS not only guarantees a high reconstruction quality but also makes a low bit-cost for compression. We integrate it in practical image coding to build up our proposed compression scheme. Experimental results show that our proposed method demonstrates excellent coding performance when used to compress image signals. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/VCIP.2018.8698724 | 2018 IEEE Visual Communications and Image Processing (VCIP) |
Keywords | Field | DocType |
Image coding,spatial domain,transform domain,down-scaling,coefficients | Compression (physics),Computer vision,Computer science,Image coding,Coding (social sciences),Artificial intelligence,Scaling,Image compression,Fold (higher-order function) | Conference |
ISBN | Citations | PageRank |
978-1-5386-4458-4 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang Cui | 1 | 1 | 0.69 |
| Shuyuan Zhu | 2 | 156 | 24.72 |
| Xiandong Meng | 3 | 15 | 6.71 |
| Shuaicheng Liu | 4 | 363 | 28.26 |
| B Zeng | 5 | 1374 | 159.35 |