# OMRA: Online Motion Resolution Adaptation to Remedy Domain Shift in Learned Hierarchical B-frame Coding

arXiv: https://arxiv.org/abs/2402.12816 · HTML: https://mlsanity.com/paper/2402.12816/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [Fast-OMRA: Fast Online Motion Resolution Adaptation for Neural B-Frame Coding](https://mlsanity.com/paper/2410.21763/) (2024)
- [Neural B-Frame Coding: Tackling Domain Shift Issues with Lightweight Online Motion Resolution Adaptation](https://mlsanity.com/paper/2511.18724/) (2025)
- [Video Compression with Graph-inspired Neural Representation](https://mlsanity.com/paper/2609.05541/) (2026)

## Abstract (arXiv)

Learned hierarchical B-frame coding aims to leverage bi-directional reference frames for better coding efficiency. However, the domain shift between training and test scenarios due to dataset limitations poses a challenge. This issue arises from training the codec with small groups of pictures (GOP) but testing it on large GOPs. Specifically, the motion estimation network, when trained on small GOPs, is unable to handle large motion at test time, incurring a negative impact on compression performance. To mitigate the domain shift, we present an online motion resolution adaptation (OMRA) method. It adapts the spatial resolution of video frames on a per-frame basis to suit the capability of the motion estimation network in a pre-trained B-frame codec. Our OMRA is an online, inference technique. It need not re-train the codec and is readily applicable to existing B-frame codecs that adopt hierarchical bi-directional prediction. Experimental results show that OMRA significantly enhances the compression performance of two state-of-the-art learned B-frame codecs on commonly used datasets.