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Conference Paper
Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model
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- Authors
- Jooyoung Lee, Seunghyun Cho, Se-Yoon Jeong, Hyoungjin Kwon, Hyunsuk Ko, Hui Yong Kim, Jin Soo Choi
- Issue Date
- 2019-06
- Citation
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2019, pp.1-4
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we propose an extended compression method using a context-adaptive entropy model. Based on the Lee et al. [11]'s approach, we extend the network structure so that compression and quality enhancement methods are jointly optimized. In terms of contexts for estimating distributions, we additionally use offset information. By exploiting the extended structure and the additional contexts, we obtain substantially improved compression performance, in terms of multi-scale structural similarity (MS-SSIM) index, compared to the model without the extensions.
- KSP Keywords
- Compression method, Compression performance, End to End(E2E), Entropy model, Extended structure, Multi-scale, Network structure, Structure Similarity Index measure(SSIM), context adaptive, image Compression, quality enhancement