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Hierarchical Autoregressive Modeling for Neural Video Compression

arXiv 2010.10258 ↗First posted 19 Oct 2020eess.IV cs.LGcited by 28

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Abstract (arXiv)

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2020.

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Citing papers per year: 2021: 7; 2022: 12; 2023: 6; 2024: 0; 2025: 3.

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  1. 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generationcited by 330
  2. 2021NeRV: Neural Representations for Videoscited by 255
  3. 2022Diffusion Probabilistic Modeling for Video Generationcited by 237
  4. 2021Deep Contextual Video Compressioncited by 216
  5. 2022Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compressioncited by 123
  6. 2021Temporal Context Mining for Learned Video Compressioncited by 112
  7. 2022An Introduction to Neural Data Compressioncited by 78
  8. 2022VCT: A Video Compression Transformercited by 74
  9. 2022FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videoscited by 46
  10. 2021Neural Video Compression using GANs for Detail Synthesis and Propagationcited by 35
  11. 2022PS-NeRV: Patch-wise Stylized Neural Representations for Videoscited by 32
  12. 2021Extending Neural P-frame Codecs for B-frame Codingcited by 30
16 more
  1. 2022Scalable Neural Video Representations with Learnable Positional Featurescited by 29
  2. 2021Progressive Neural Image Compression with Nested Quantization and Latent Orderingcited by 24
  3. 2023Computationally-Efficient Neural Image Compression with Shallow Decoderscited by 22
  4. 2022TINC: Tree-structured Implicit Neural Compressioncited by 14
  5. 2021Towards Empirical Sandwich Bounds on the Rate-Distortion Functioncited by 13
  6. 2022Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspectivecited by 10
  7. 2023On the Choice of Perception Loss Function for Learned Video Compressioncited by 10
  8. 2022Bit Allocation using Optimizationcited by 9
  9. 2023MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Codingcited by 9
  10. 2023Scene Matters: Model-based Deep Video Compressioncited by 6
  11. 2023Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrapperscited by 6
  12. 2025MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidancecited by 3
  13. 2022Correcting the Sub-optimal Bit Allocationcited by 1
  14. 2022Latent Discretization for Continuous-time Sequence Compressioncited by 0
  15. 2025On Self-Adaptive Perception Loss Function for Sequential Lossy Compressioncited by 0
  16. 2025AstroCompress: A benchmark dataset for multi-purpose compression of astronomical datacited by 0