BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression

TL;DR

BiECVC introduces a novel bidirectional video compression framework that enhances context modeling and gating, achieving state-of-the-art compression efficiency surpassing traditional codecs across standard datasets.

Contribution

The paper proposes BiECVC, a new learned BVC method with diversified local and non-local context modeling and adaptive gating, outperforming existing methods and surpassing VTM 13.2 in all tests.

Findings

Reduces bit-rate by up to 15.7% compared to VTM 13.2.

First learned video codec to outperform VTM 13.2 RA on all standard datasets.

Achieves state-of-the-art compression performance in learned video coding.

Abstract

Recent forward prediction-based learned video compression (LVC) methods have achieved impressive results, even surpassing VVC reference software VTM under the Low Delay B (LDB) configuration. In contrast, learned bidirectional video compression (BVC) remains underexplored and still lags behind its forward-only counterparts. This performance gap is mainly due to the limited ability to extract diverse and accurate contexts: most existing BVCs primarily exploit temporal motion while neglecting non-local correlations across frames. Moreover, they lack the adaptability to dynamically suppress harmful contexts arising from fast motion or occlusion. To tackle these challenges, we propose BiECVC, a BVC framework that incorporates diversified local and non-local context modeling along with adaptive context gating. For local context enhancement, BiECVC reuses high-quality features from lower layers and aligns them using decoded motion vectors without introducing extra motion overhead. To model non-local dependencies efficiently, we adopt a linear attention mechanism that balances performance and complexity. To further mitigate the impact of inaccurate context prediction, we introduce Bidirectional Context Gating, inspired by data-dependent decay in recent autoregressive language models, to dynamically filter contextual information based on conditional coding results. Extensive experiments demonstrate that BiECVC achieves state-of-the-art performance, reducing the bit-rate by 13.4% and 15.7% compared to VTM 13.2 under the Random Access (RA) configuration with intra periods of 32 and 64, respectively. To our knowledge, BiECVC is the first learned video codec to surpass VTM 13.2 RA across all standard test datasets.