The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules
Open accessAbstract
Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM.
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Cites
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Overview of the High Efficiency Video Coding (HEVC) Standard
Sullivan, G. J., Ohm, J.-R., Han, W.-J., & Wiegand, T. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2221191 · Cited by 8,263
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Multiscale structural similarity for image quality assessment
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“To achieve high subjective quality, we also train the model using MS-SSIM quality metrics [33] and then distortion term is defined by D ( x , ˆ x ) = 1 − MS - SSIM( x , ˆ x ) .”
MethodThe JPEG still picture compression standard
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The JPEG still picture compression standard
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Conditional Image Generation with PixelCNN Decoders
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05328 · Cited by 1,833
Variational image compression with a scale hyperprior
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End-to-end Optimized Image Compression
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Cited by
He, D., Yang, Z., Peng, W., Ma, R., Qin, H., & Wang, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00563 · Cited by 455
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., & Furon, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02053 · Cited by 251
Metadata
- Authors
- Cheng, Z., Sun, H., Takeuchi, M., & Katto, J.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 7936-7945
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034469748
- Cited by
- 1,161
- References
- 27
- FWCI
- 36.74
- Type
- conference paper
- Language
- en
- Open access
- green
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- Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2020). Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00796
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