This paper provides a quantitative analysis of intra coding tools developed for the Enhanced Compression Model (ECM), which is the reference software for the next-generation video codec being explored by the Joint Video Experts Team (JVET). The analysis focuses on the selection rate of various luma and chroma intra coding tools across different ECM versions, video resolutions, and bitrates, offering insights to the standardization community.
Ensemble-based decoders can effectively capture the predictive uncertainty in deep learning-based video compression models, leading to improved rate-distortion performance.
Efficient low-latency stereo video compression using neural networks.
提案されたIBVCは、ビデオフレーム補間とアーティファクト削減圧縮を組み合わせた効率的なBフレームビデオ圧縮手法であり、優れた再構成性能と効率を実現しています。
Proposing a joint local and global motion compensation module (LGMC) for learned video compression to address the limitations of existing models.
提案された特徴変調技術により、NVCの進化における重要な問題が解決されました。
IBVC proposes an efficient video compression approach by combining video frame interpolation and artifact reduction, achieving superior performance compared to state-of-the-art methods.
The author proposes a joint local and global motion compensation module (LGMC) to enhance learned video compression by addressing the limitations of existing methods. By combining flow net for local motion compensation and cross attention for global context, the LGMC significantly improves rate-distortion performance over baseline models.
The author introduces NeRV++, an improved neural video representation, to enhance video compression efficiency by refining the decoder architecture and achieving superior performance in comparison to existing methods.