# A Neural-network Enhanced Video Coding Framework beyond ECM

arXiv: https://arxiv.org/abs/2402.08397 · HTML: https://mlsanity.com/paper/2402.08397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Context-adaptive neural network based prediction for image compression](https://mlsanity.com/paper/1807.06244/) (2018)
- [Sub-sampled Cross-component Prediction for Emerging Video Coding Standards](https://mlsanity.com/paper/2012.15067/) (2020)

## Built on by (most cited first, 1 of 1)

- [Generative Latent Video Compression](https://mlsanity.com/paper/2510.09987/) (2025)

## Abstract (arXiv)

In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. Compared with ECM-10.0, our method achieves 6.26, 13.33, and 12.33 BD-rate savings for the Y, U, and V components under random access (RA) configuration, respectively.