mlsanityData to 14 Oct 2026

DVC: An End-to-end Deep Video Compression Framework

arXiv 1812.00101 ↗First posted 30 Nov 2018eess.IV cs.CVcited by 332

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Abstract (arXiv)

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information. In this paper, taking advantage of both classical architecture in the conventional video compression method and the powerful non-linear representation ability of neural networks, we propose the first end-to-end video compression deep model that jointly optimizes all the components for video compression. Specifically, learning based optical flow estimation is utilized to obtain the motion information and reconstruct the current frames. Then we employ two auto-encoder style neural networks to compress the corresponding motion and residual information. All the modules are jointly learned through a single loss function, in which they collaborate with each other by considering the trade-off between reducing the number of compression bits and improving quality of the decoded video. Experimental results show that the proposed approach can outperform the widely used video coding standard H.264 in terms of PSNR and be even on par with the latest standard H.265 in terms of MS-SSIM. Code is released at https://github.com/GuoLusjtu/DVC.

Key facts. Cited by 332 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2018.

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Citing papers per year: 2020: 1; 2021: 39; 2022: 66; 2023: 56; 2024: 60; 2025: 56; 2026: 54.

332 papers.

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  1. 2023Finite Scalar Quantization: VQ-VAE Made Simplecited by 440
  2. 2021NeRV: Neural Representations for Videoscited by 256
  3. 2022Diffusion Probabilistic Modeling for Video Generationcited by 238
  4. 2021Deep Contextual Video Compressioncited by 221
  5. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 175
  6. 2023Neural Video Compression with Diverse Contextscited by 165
  7. 2021FVC: A New Framework towards Deep Video Compression in Feature Spacecited by 153
  8. 2022Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compressioncited by 128
  9. 2024Neural Video Compression with Feature Modulationcited by 123
  10. 2024Image and Video Tokenization with Binary Spherical Quantizationcited by 112
  11. 2023HNeRV: A Hybrid Neural Representation for Videoscited by 100
  12. 2023VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNetcited by 83
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  1. 2022COIN++: Neural Compression Across Modalitiescited by 82
  2. 2025Towards Practical Real-Time Neural Video Compressioncited by 80
  3. 2022An Introduction to Neural Data Compressioncited by 79
  4. 2022VCT: A Video Compression Transformercited by 74
  5. 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representationcited by 71
  6. 2021ELF-VC: Efficient Learned Flexible-Rate Video Codingcited by 69
  7. 2021VoxelContext-Net: An Octree based Framework for Point Cloud Compressioncited by 61
  8. 2023Dense Optical Tracking: Connecting the Dotscited by 61
  9. 2022Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Predictioncited by 55
  10. 2021Implicit Neural Video Compressioncited by 52
  11. 2022Wireless Deep Video Semantic Transmissioncited by 50
  12. 2022CANF-VC: Conditional Augmented Normalizing Flows for Video Compressioncited by 46
  13. 2022FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videoscited by 46
  14. 2023MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videoscited by 46
  15. 2021DeepWiVe: Deep-Learning-Aided Wireless Video Transmissioncited by 45
  16. 2023DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videoscited by 42
  17. 2021Perceptual Learned Video Compression with Recurrent Conditional GANcited by 41
  18. 2021Overfitting for Fun and Profit: Instance-Adaptive Data Compressioncited by 38
  19. 2023Towards Scalable Neural Representation for Diverse Videoscited by 37
  20. 2024USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020scited by 37
  21. 2021Neural Video Compression using GANs for Detail Synthesis and Propagationcited by 36
  22. 2021MarioNette: Self-Supervised Sprite Learningcited by 35
  23. 2023A Residual Diffusion Model for High Perceptual Quality Codec Augmentationcited by 35
  24. 2022PS-NeRV: Patch-wise Stylized Neural Representations for Videoscited by 32
  25. 2023SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representationscited by 31
  26. 2024HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compressioncited by 31
  27. 2021Extending Neural P-frame Codecs for B-frame Codingcited by 30
  28. 2022Scalable Neural Video Representations with Learnable Positional Featurescited by 29
  29. 2024Boosting Neural Representations for Videos with a Conditional Decodercited by 29
  30. 2024NVRC: Neural Video Representation Compressioncited by 29
  31. 2021End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compressioncited by 28
  32. 20254DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Videocited by 27
  33. 2021Efficient Video Compression via Content-Adaptive Super-Resolutioncited by 26
  34. 2023Compression with Bayesian Implicit Neural Representationscited by 25
  35. 2024DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codescited by 25
  36. 2025Generative Latent Coding for Ultra-Low Bitrate Image and Video Compressioncited by 25
  37. 2021Progressive Neural Image Compression with Nested Quantization and Latent Orderingcited by 24
  38. 2022Imposing Consistency for Optical Flow Estimationcited by 24

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