# Learned Video Compression with Feature-level Residuals

arXiv: https://arxiv.org/abs/2004.08283 · HTML: https://mlsanity.com/paper/2004.08283/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos](https://mlsanity.com/paper/2304.04452/) (2023)
- [Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction](https://mlsanity.com/paper/2206.07460/) (2022)
- [Content Adaptive Latents and Decoder for Neural Image Compression](https://mlsanity.com/paper/2212.10132/) (2022)
- [Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding](https://mlsanity.com/paper/2304.02690/) (2023)
- [Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression](https://mlsanity.com/paper/2112.13309/) (2021)
- [Versatile Learned Video Compression](https://mlsanity.com/paper/2111.03386/) (2021)
- [Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks](https://mlsanity.com/paper/2201.10162/) (2022)
- [Video Coding Using Learned Latent GAN Compression](https://mlsanity.com/paper/2207.04324/) (2022)
- [Hierarchical Vector-Quantized Latents for Perceptual Low-Resolution Video Compression](https://mlsanity.com/paper/2512.24547/) (2025)
- [Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy](https://mlsanity.com/paper/2210.08391/) (2022)
- [Lightweight Hybrid Video Compression Framework Using Reference-Guided Restoration Network](https://mlsanity.com/paper/2303.11592/) (2023)
- [Blurry Video Compression: A Trade-off between Visual Enhancement and Data Compression](https://mlsanity.com/paper/2311.04430/) (2023)
- [Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment](https://mlsanity.com/paper/2512.10450/) (2025)

## Abstract (arXiv)

In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. First, we notice that pixel space residuals is sensitive to the prediction errors of optical flow based motion compensation. To suppress the relative influence, we propose to compress the residuals of image feature rather than the residuals of image pixels. Furthermore, we combine the advantages of both pixel-level and feature-level residual compression methods by model ensembling. Finally, we propose a step-by-step training strategy to improve the training efficiency of the whole framework. Experiment results indicate that our proposed method achieves 0.9968 MS-SSIM on CLIC validation set and 0.9967 MS-SSIM on test set.