mlsanityData to 7 Oct 2026

Deep Residual Learning for Image Compression

arXiv 1906.09731 ↗First posted 24 Jun 2019eess.IVcited by 13

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Abstract (arXiv)

In this paper, we provide a detailed description on our approach designed for CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our approach mainly consists of two proposals, i.e. deep residual learning for image compression and sub-pixel convolution as up-sampling operations. Experimental results have indicated that our approaches, Kattolab, Kattolabv2 and KattolabSSIM, achieve 0.972 in MS-SSIM at the rate constraint of 0.15bpp with moderate complexity during the validation phase.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2019.

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Citing papers per year: 2021: 2; 2022: 6; 2023: 3; 2024: 2.

13 papers.

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  1. 2022ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Codingcited by 296
  2. 2021Joint Global and Local Hierarchical Priors for Learned Image Compressioncited by 52
  3. 2022RGB no more: Minimally-decoded JPEG Vision Transformerscited by 18
  4. 2022Device Interoperability for Learned Image Compression with Weights and Activations Quantizationcited by 8
  5. 2022Exploring Structural Sparsity in Neural Image Compressioncited by 4
  6. 2022Video Coding Using Learned Latent GAN Compressioncited by 4
  7. 2021A New Image Codec Paradigm for Human and Machine Usescited by 3
  8. 2023Exploring Effective Mask Sampling Modeling for Neural Image Compressioncited by 2
  9. 2024Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimizationcited by 2
  10. 2023Bandwidth-efficient Inference for Neural Image Compressioncited by 1
  11. 2023Task-driven Compression for Collision Encoding based on Depth Imagescited by 1
  12. 2024PatchSVD: A Non-uniform SVD-based Image Compression Algorithmcited by 1
1 more
  1. 2022Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modulescited by 0