Name
Abstract | ||
|---|---|---|
In this paper, we provide a detailed description on our submitted method Kattolab to Workshop and Challenge on Learned Image Compression (CLIC) 2020. Our method mainly incorporates discretized Gaussian Mixture Likeli-hoods to previous state-of-the-art learned compression algorithms. Besides, we also describes the acceleration strategies and bit optimization with the low-rate constraint. Experimental results have demonstrated that our approach Kattolab achieves 0.9761 in terms of MS-SSIM at the rate constraint of 0.15 bpp during the validation phase. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPRW50498.2020.00071 | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | DocType | ISSN |
CLIC,acceleration strategies,bit optimization,low-rate constraint,approach Kattolab,low bitrate image compression,discretized Gaussian mixture,Gaussian mixture likelihoods,learned image compression 2020,state-of-the-art learned compression algorithms | Conference | 2160-7508 |
ISBN | Citations | PageRank |
978-1-7281-9361-8 | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengxue Cheng | 1 | 28 | 10.45 |
| Heming Sun | 2 | 92 | 22.50 |
| Jiro Katto | 3 | 262 | 66.14 |