# Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding?

arXiv: https://arxiv.org/abs/2007.08922 · HTML: https://mlsanity.com/paper/2007.08922/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [FitVid: Overfitting in Pixel-Level Video Prediction](https://mlsanity.com/paper/2106.13195/) (2021)
- [DFPN: Deformable Frame Prediction Network](https://mlsanity.com/paper/2105.12794/) (2021)
- [A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space](https://mlsanity.com/paper/2104.00807/) (2021)
- [FG-DFPN: Flow Guided Deformable Frame Prediction Network](https://mlsanity.com/paper/2503.11343/) (2025)

## Abstract (arXiv)

Given recent advances in learned video prediction, we investigate whether a simple video codec using a pre-trained deep model for next frame prediction based on previously encoded/decoded frames without sending any motion side information can compete with standard video codecs based on block-motion compensation. Frame differences given learned frame predictions are encoded by a standard still-image (intra) codec. Experimental results show that the rate-distortion performance of the simple codec with symmetric complexity is on average better than that of x264 codec on 10 MPEG test videos, but does not yet reach the level of x265 codec. This result demonstrates the power of learned frame prediction (LFP), since unlike motion compensation, LFP does not use information from the current picture. The implications of training with L1, L2, or combined L2 and adversarial loss on prediction performance and compression efficiency are analyzed.