BROWSE
Detail
Conference Paper
Low Bit-rate Image Compression based on Post-processing with Grouped Residual Dense Network
Cited - time in 
Share 
Share
- Authors
- Seunghyun Cho, Jooyoung Lee, Jongho Kim, Younhee Kim, Dong-Wook Kim, Jae Ryun Chung, Seung-Won Jung
- Issue Date
- 2019-06
- Citation
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2019, pp.1-5
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, an image compression method implemented for CVPR 2019 Challenge on Learned Image Compression (CLIC) is introduced. It is designed to satisfy both requirements of image compression, "higher compression ratio" and "better quality", at the same time. To this end, a neural network based image quality enhancement is incorporated into the most recent traditional image/video coding technique. The decoders, ETRIDGU, ETRIDGUlite, and ETRIDGUfast, which implement the proposed image compression method are designed to have different degrees of complexity and compression efficiency. ETRIDGU, which provides the highest compression efficiency, is reported to achieve the 2nd highest PSNR in the lowrate track of CLIC. ETRIDGUlite, which compromises between the compression efficiency and the complexity, is reported to be the fastest one among the decoders with high mean opinion score (MOS) in the same track.
- KSP Keywords
- Compression method, Dense network, Image Compression, Image quality enhancement, Post-Processing, Video coding, compression efficiency, compression ratio, low bit rate, mean opinion score(MOS), neural network