Name
Abstract | ||
|---|---|---|
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance, and requires to cope with the spatial variation of image content and contextual dependence among learned codes. Traditional entropy models can spatially adapt the local bit rate based on the image content, but usually are limited in exploiting context in code space. On the other hand, mos... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TPAMI.2020.2983926 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Image coding,Entropy,Context modeling,Adaptation models,Decoding,Quantization (signal),Bit rate | Journal | 43 |
Issue | ISSN | Citations |
10 | 0162-8828 | 3 |
PageRank | References | Authors |
0.39 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| mu li | 1 | 21 | 2.09 |
| Wangmeng Zuo | 2 | 3833 | 173.11 |
| Shuhang Gu | 3 | 701 | 28.25 |
| Jane You | 4 | 1885 | 136.93 |
| David Zhang | 5 | 2337 | 102.40 |