The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
Open accessAbstract
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders. They are promising to be large-scale adopted. For the sake of practicality, a thorough investigation of the architecture design of learned image compression, regarding both compression performance and running speed, is essential. In this paper, we first propose uneven channel-conditional adaptive coding, motivated by the observation of energy compaction in learned image compression. Combining the proposed uneven grouping model with existing context models, we obtain a spatial-channel contextual adaptive model to improve the coding performance without damage to running speed. Then we study the structure of the main transform and propose an efficient model, ELIC, to achieve state-of-the-art speed and compression ability. With superior performance, the proposed model also supports extremely fast preview decoding and progressive decoding, which makes the coming application of learning-based image compression more promising.
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Cites
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Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules
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Metadata
- Authors
- He, D., Yang, Z., Peng, W., Ma, R., Qin, H., & Wang, Y.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 5708-5717
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312806968
- Cited by
- 455
- References
- 40
- FWCI
- 33.32
- Type
- conference paper
- Language
- en
- Open access
- green
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- He, D., Yang, Z., Peng, W., Ma, R., Qin, H., & Wang, Y. (2022). ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.00563
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