Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914342710018048 |
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| author | Sheng, Xihua Chen, Peilin Wang, Meng Zhang, Li Wang, Shiqi Wu, Dapeng Oliver |
| author_facet | Sheng, Xihua Chen, Peilin Wang, Meng Zhang, Li Wang, Shiqi Wu, Dapeng Oliver |
| contents | With the remarkable progress in neural P-frame video coding, neural B-frame coding has recently emerged as a critical research direction. However, most existing neural B-frame codecs directly adopt P-frame coding tools without adequately addressing the unique challenges of B-frame compression, leading to suboptimal performance. To bridge this gap, we propose novel enhancements for motion compression and temporal fusion for neural B-frame coding. First, we design a fine-grained motion compression method. This method incorporates an interactive dual-branch motion auto-encoder with per-branch adaptive quantization steps, which enables fine-grained compression of bi-directional motion vectors while accommodating their asymmetric bitrate allocation and reconstruction quality requirements. Furthermore, this method involves an interactive motion entropy model that exploits correlations between bi-directional motion latent representations by interactively leveraging partitioned latent segments as directional priors. Second, we propose a selective temporal fusion method that predicts bi-directional fusion weights to achieve discriminative utilization of bi-directional multi-scale temporal contexts with varying qualities. Additionally, this method introduces a hyperprior-based implicit alignment mechanism for contextual entropy modeling. By treating the hyperprior as a surrogate for the contextual latent representation, this mechanism implicitly mitigates the misalignment in the fused bi-directional temporal priors. Extensive experiments demonstrate that our proposed codec achieves an average BD-rate reduction of approximately 10% compared to the state-of-the-art neural B-frame codec, DCVC-B, and delivers comparable or even superior compression performance to the H.266/VVC reference software under random-access configurations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_07709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding Sheng, Xihua Chen, Peilin Wang, Meng Zhang, Li Wang, Shiqi Wu, Dapeng Oliver Image and Video Processing Computer Vision and Pattern Recognition With the remarkable progress in neural P-frame video coding, neural B-frame coding has recently emerged as a critical research direction. However, most existing neural B-frame codecs directly adopt P-frame coding tools without adequately addressing the unique challenges of B-frame compression, leading to suboptimal performance. To bridge this gap, we propose novel enhancements for motion compression and temporal fusion for neural B-frame coding. First, we design a fine-grained motion compression method. This method incorporates an interactive dual-branch motion auto-encoder with per-branch adaptive quantization steps, which enables fine-grained compression of bi-directional motion vectors while accommodating their asymmetric bitrate allocation and reconstruction quality requirements. Furthermore, this method involves an interactive motion entropy model that exploits correlations between bi-directional motion latent representations by interactively leveraging partitioned latent segments as directional priors. Second, we propose a selective temporal fusion method that predicts bi-directional fusion weights to achieve discriminative utilization of bi-directional multi-scale temporal contexts with varying qualities. Additionally, this method introduces a hyperprior-based implicit alignment mechanism for contextual entropy modeling. By treating the hyperprior as a surrogate for the contextual latent representation, this mechanism implicitly mitigates the misalignment in the fused bi-directional temporal priors. Extensive experiments demonstrate that our proposed codec achieves an average BD-rate reduction of approximately 10% compared to the state-of-the-art neural B-frame codec, DCVC-B, and delivers comparable or even superior compression performance to the H.266/VVC reference software under random-access configurations. |
| title | Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.07709 |