mlsanityData to 7 Oct 2026

Context-adaptive Entropy Model for End-to-end Optimized Image Compression

arXiv 1809.10452 ↗First posted 27 Sep 2018eess.IVcited by 138

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Abstract (arXiv)

We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index.

Key facts. Cited by 138 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2018.

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Citing papers per year: 2021: 33; 2022: 29; 2023: 28; 2024: 26; 2025: 17; 2026: 5.

138 papers.

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  1. 2021COIN: COmpression with Implicit Neural representationscited by 232
  2. 2021Checkerboard Context Model for Efficient Learned Image Compressioncited by 193
  3. 2021Implicit Neural Representations for Image Compressioncited by 161
  4. 2021Enhanced Invertible Encoding for Learned Image Compressioncited by 102
  5. 2022Entroformer: A Transformer-based Entropy Model for Learned Image Compressioncited by 102
  6. 2022COIN++: Neural Compression Across Modalitiescited by 81
  7. 2022MLIC: Multi-Reference Entropy Model for Learned Image Compressioncited by 80
  8. 2022An Introduction to Neural Data Compressioncited by 78
  9. 2021Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transformcited by 56
  10. 2021Joint Global and Local Hierarchical Priors for Learned Image Compressioncited by 52
  11. 2021Lossy Compression for Lossless Predictioncited by 51
  12. 2022Unified Multivariate Gaussian Mixture for Efficient Neural Image Compressioncited by 43
38 more
  1. 2022Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compressioncited by 42
  2. 2023Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Triggercited by 40
  3. 2021Slimmable Compressive Autoencoders for Practical Neural Image Compressioncited by 38
  4. 2021Perceptual Learned Video Compression with Recurrent Conditional GANcited by 38
  5. 2022Towards Semantic Communications: Deep Learning-Based Image Semantic Codingcited by 35
  6. 2021Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modulescited by 33
  7. 2022Neural Data-Dependent Transform for Learned Image Compressioncited by 29
  8. 2024Image Compression for Machine and Human Vision with Spatial-Frequency Adaptationcited by 26
  9. 2021DPICT: Deep Progressive Image Compression Using Trit-Planescited by 25
  10. 2021Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuningcited by 24
  11. 2023Modality-Agnostic Variational Compression of Implicit Neural Representationscited by 22
  12. 2024CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformercited by 22
  13. 2025Generative Latent Coding for Ultra-Low Bitrate Image and Video Compressioncited by 22
  14. 2023Context-Based Trit-Plane Coding for Progressive Image Compressioncited by 21
  15. 2025Generative Latent Coding for Ultra-Low Bitrate Image Compressioncited by 21
  16. 2021ANFIC: Image Compression Using Augmented Normalizing Flowscited by 20
  17. 2023LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compressioncited by 19
  18. 2022B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flowscited by 18
  19. 2023Raw Image Reconstruction with Learned Compact Metadatacited by 18
  20. 2021Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeletoncited by 16
  21. 2021Learning Scalable ℓ_∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compressioncited by 16
  22. 2022Selective compression learning of latent representations for variable-rate image compressioncited by 16
  23. 2024Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Modelcited by 16
  24. 2022Neural Distributed Image Compression with Cross-Attention Feature Alignmentcited by 15
  25. 2024Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptationcited by 15
  26. 2022Advancing Learned Video Compression with In-loop Frame Predictioncited by 14
  27. 2023Multi-Modality Deep Network for Extreme Learned Image Compressioncited by 14
  28. 2023ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Trainingcited by 13
  29. 2023Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compressioncited by 13
  30. 2023End-to-End Learnable Multi-Scale Feature Compression for VCMcited by 13
  31. 2021Interpolation variable rate image compressioncited by 12
  32. 2021LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Networkcited by 12
  33. 2021Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compressioncited by 12
  34. 2022Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utilitycited by 12
  35. 2022Content-oriented learned image compressioncited by 12
  36. 2024HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compressioncited by 12
  37. 2021Neural Distributed Image Compression using Common Informationcited by 11
  38. 2022Universal Deep Image Compression via Content-Adaptive Optimization with Adapterscited by 11

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