mlsanityData to 7 Oct 2026

CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

arXiv 2011.03029 ↗First posted 5 Nov 2020cs.CV eess.IVcited by 271

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Abstract (arXiv)

This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs. In particular, CompressAI includes pre-trained models and evaluation tools to compare learned methods with traditional codecs. Multiple models from the state-of-the-art on learned end-to-end compression have thus been reimplemented in PyTorch and trained from scratch. We also report objective comparison results using PSNR and MS-SSIM metrics vs. bit-rate, using the Kodak image dataset as test set. Although this framework currently implements models for still-picture compression, it is intended to be soon extended to the video compression domain.

Key facts. Cited by 271 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2020.

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Citing papers per year: 2021: 22; 2022: 41; 2023: 52; 2024: 79; 2025: 43; 2026: 34.

271 papers.

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  1. 2022ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Codingcited by 296
  2. 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 289
  3. 2021COIN: COmpression with Implicit Neural representationscited by 232
  4. 2021Deep Contextual Video Compressioncited by 216
  5. 2022Lossy Image Compression with Conditional Diffusion Modelscited by 165
  6. 2022The Devil Is in the Details: Window-based Attention for Image Compressioncited by 141
  7. 2023Invisible Image Watermarks Are Provably Removable Using Generative AIcited by 124
  8. 2021Temporal Context Mining for Learned Video Compressioncited by 112
  9. 2024Image and Video Tokenization with Binary Spherical Quantizationcited by 110
  10. 2021Enhanced Invertible Encoding for Learned Image Compressioncited by 102
  11. 2024GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splattingcited by 97
  12. 2023Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Modelscited by 90
38 more
  1. 2022COIN++: Neural Compression Across Modalitiescited by 81
  2. 2022MLIC: Multi-Reference Entropy Model for Learned Image Compressioncited by 80
  3. 2024CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splattingcited by 80
  4. 2021Transformer-based Image Compressioncited by 60
  5. 2021Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transformcited by 56
  6. 2023MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compressioncited by 56
  7. 2023Frequency-Aware Transformer for Learned Image Compressioncited by 56
  8. 2023C3: High-performance and low-complexity neural compression from a single image or videocited by 54
  9. 2021Joint Global and Local Hierarchical Priors for Learned Image Compressioncited by 52
  10. 2021Lossy Compression for Lossless Predictioncited by 51
  11. 2022CANF-VC: Conditional Augmented Normalizing Flows for Video Compressioncited by 44
  12. 2022Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compressioncited by 42
  13. 2022Density-preserving Deep Point Cloud Compressioncited by 39
  14. 2024USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020scited by 37
  15. 2023A Residual Diffusion Model for High Perceptual Quality Codec Augmentationcited by 33
  16. 2022Lossy Image Compression with Quantized Hierarchical VAEscited by 32
  17. 2021Supervised Compression for Resource-Constrained Edge Computing Systemscited by 31
  18. 2024Boosting Neural Representations for Videos with a Conditional Decodercited by 29
  19. 2024WMAdapter: Adding WaterMark Control to Latent Diffusion Modelscited by 29
  20. 2025Learned Image Compression with Dictionary-based Entropy Modelcited by 29
  21. 2021End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compressioncited by 28
  22. 2024Idempotence and Perceptual Image Compressioncited by 27
  23. 2024LaWa: Using Latent Space for In-Generation Image Watermarkingcited by 27
  24. 2023HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methodscited by 26
  25. 2024Image Compression for Machine and Human Vision with Spatial-Frequency Adaptationcited by 26
  26. 20254DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Videocited by 26
  27. 2021DPICT: Deep Progressive Image Compression Using Trit-Planescited by 25
  28. 2021Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuningcited by 24
  29. 2023Computationally-Efficient Neural Image Compression with Shallow Decoderscited by 22
  30. 2025Linear Attention Modeling for Learned Image Compressioncited by 22
  31. 2023Context-Based Trit-Plane Coding for Progressive Image Compressioncited by 21
  32. 2025Learned Image Compression with Hierarchical Progressive Context Modelingcited by 20
  33. 2023LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compressioncited by 19
  34. 2024Causal Context Adjustment Loss for Learned Image Compressioncited by 19
  35. 2022B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flowscited by 18
  36. 2025Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inferencecited by 18
  37. 2023Offline and Online Optical Flow Enhancement for Deep Video Compressioncited by 17
  38. 2024Image Coding for Machines with Edge Information Learning Using Segment Anythingcited by 17

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