DCVC-MB: Neural B-Frame Video Compression using State Space Models
This paper proposes DCVC-Mamba (DCVC-MB), a neural video codec framework for B-frame coding. It incorporates an IBP frame strategy for low-delay B-frame coding, a spatio-temporal fusion model based on state-space models for bidirectional temporal prediction, and an entropy-aware skipping mechanism that selectively omits coding certain latents to reduce entropy coding times. Two inference-time strategies are also implemented to enhance compression performance. Experimental evaluation shows that DCVC-MB achieves average BD-rate reductions of up to 8.98% compared to prior neural video codecs, and improvements of up to 30.45% and 1.81% over the VTM-19.0-LDP and VTM-19.0-RA (Inter-GoP=16) benchmarks, respectively, contributing to advances in neural video compression.
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[Submitted on 15 Jul 2026]
Title:DCVC-MB: Neural B-Frame Video Compression using State Space Models
View a PDF of the paper titled DCVC-MB: Neural B-Frame Video Compression using State Space Models, by Arjun Arora and 9 other authors
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Abstract:In this paper we propose DCVC-Mamba (DCVC-MB), a neural video codec framework for B-frame coding. Our approach incorporates an IBP frame strategy for low-delay B-frame coding, a spatio-temporal fusion model based on state-space models for bidirectional temporal prediction, and an entropy-aware skipping mechanism that selectively omits coding certain latents to reduce entropy coding times. In addition to our model contributions we also implement two inference-time strategies that enhance compression performance. Experimental evaluation shows that DCVC-MB compares favorably to existing NVCs and traditional codecs. The method demonstrates BD-rate reductions of up to $8.98\%$ on average compared to prior neural video codecs, and improvements of up to $30.45\%$ and $1.81\%$ over the VTM-19.0-LDP and VTM-19.0-RA(Inter-GoP=16) benchmarks, respectively, contributing to advances in neural video compression.
Comments: Accepted to ICME 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.14305 [cs.CV]
(or arXiv:2607.14305v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.14305
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Calvin-Khang Ta [view email] [v1] Wed, 15 Jul 2026 19:09:24 UTC (6,782 KB)
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