# Residual Learning and Filtering Networks for End-to-End Lossless Video Compression

arXiv: https://arxiv.org/abs/2503.08819 · HTML: https://mlsanity.com/paper/2503.08819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Video Compression through Image Interpolation](https://mlsanity.com/paper/1804.06919/) (2018)
- [M-LVC: Multiple Frames Prediction for Learned Video Compression](https://mlsanity.com/paper/2004.10290/) (2020)
- [Compressed Video Action Recognition](https://mlsanity.com/paper/1712.00636/) (2017)
- [Video Compression With Rate-Distortion Autoencoders](https://mlsanity.com/paper/1908.05717/) (2019)
- [Content Adaptive and Error Propagation Aware Deep Video Compression](https://mlsanity.com/paper/2003.11282/) (2020)
- [Improving Deep Video Compression by Resolution-adaptive Flow Coding](https://mlsanity.com/paper/2009.05982/) (2020)
- [End-to-end Optimized Video Compression with MV-Residual Prediction](https://mlsanity.com/paper/2005.12945/) (2020)

## Abstract (arXiv)

Existing learning-based video compression methods still face challenges related to inaccurate motion estimates and inadequate motion compensation structures. These issues result in compression errors and a suboptimal rate-distortion trade-off. To address these challenges, this work presents an end-to-end video compression method that incorporates several key operations. Specifically, we propose an autoencoder-type network with a residual skip connection to efficiently compress motion information. Additionally, we design motion vector and residual frame filtering networks to mitigate compression errors in the video compression system. To improve the effectiveness of the motion compensation network, we utilize powerful nonlinear transforms, such as the Parametric Rectified Linear Unit (PReLU), to delve deeper into the motion compensation architecture. Furthermore, a buffer is introduced to fine-tune the previous reference frames, thereby enhancing the reconstructed frame quality. These modules are combined with a carefully designed loss function that assesses the trade-off and enhances the overall video quality of the decoded output. Experimental results showcase the competitive performance of our method on various datasets, including HEVC (sequences B, C, and D), UVG, VTL, and MCL-JCV. The proposed approach tackles the challenges of accurate motion estimation and motion compensation in video compression, and the results highlight its competitive performance compared to existing methods.