The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Open accessAbstract
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. Finally, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to multiple metrics. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well as recently published methods based on deep neural networks.
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Cites
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“This dB transform is also supported by the original MS-SSIM [27] publication, which showed the mean opinion score is linearly correlated with the MS-SSIM score after transforming that score to the log domain.”
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Cited by
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules
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Conditional Probability Models for Deep Image Compression
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DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback
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Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
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Chen, T., Liu, H., Ma, Z., Shen, Q., Cao, X., & Wang, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3058615 · Cited by 346
Metadata
- Authors
- Johnston, N., Vincent, D., Minnen, D., Covell, M., Singh, S., Chinen, T., Jin Hwang, S., Shor, J., & Toderici, G.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4385-4393
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2597747080
- Cited by
- 366
- References
- 19
- FWCI
- 14.92
- Type
- conference paper
- Language
- en
- Open access
- green
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- Johnston, N., Vincent, D., Minnen, D., Covell, M., Singh, S., Chinen, T., Jin Hwang, S., Shor, J., & Toderici, G. (2018). Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00461
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