Neural Video Compression with Context Modulation

• URL: http://arxiv.org/abs/2505.14541v1
• Date: Tue, 20 May 2025 15:57:09 GMT
• Title: Neural Video Compression with Context Modulation
• Authors: Chuanbo Tang, Zhuoyuan Li, Yifan Bian, Li Li, Dong Liu,
• Abstract summary: In this paper, we address the limitation by modulating the temporal context with the reference frame in two steps.<n>We achieve on average 22.7% reduction over the advanced traditional video H.266/VVC, and offer an average 10.1% saving over the previous state-of-the-art NVC DCVC-FM.
• Score: 9.875413481663742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient video coding is highly dependent on exploiting the temporal redundancy, which is usually achieved by extracting and leveraging the temporal context in the emerging conditional coding-based neural video codec (NVC). Although the latest NVC has achieved remarkable progress in improving the compression performance, the inherent temporal context propagation mechanism lacks the ability to sufficiently leverage the reference information, limiting further improvement. In this paper, we address the limitation by modulating the temporal context with the reference frame in two steps. Specifically, we first propose the flow orientation to mine the inter-correlation between the reference frame and prediction frame for generating the additional oriented temporal context. Moreover, we introduce the context compensation to leverage the oriented context to modulate the propagated temporal context generated from the propagated reference feature. Through the synergy mechanism and decoupling loss supervision, the irrelevant propagated information can be effectively eliminated to ensure better context modeling. Experimental results demonstrate that our codec achieves on average 22.7% bitrate reduction over the advanced traditional video codec H.266/VVC, and offers an average 10.1% bitrate saving over the previous state-of-the-art NVC DCVC-FM. The code is available at https://github.com/Austin4USTC/DCMVC.

Related papers

• READ: Real-time and Efficient Asynchronous Diffusion for Audio-driven Talking Head Generation [55.58089937219475]
  We propose READ, the first real-time diffusion-transformer-based talking head generation framework.<n>Our approach first learns highly compressed video latent space via a VAE, significantly reducing the token count to speech generation.<n>We show that READ outperforms state-of-the-art methods by generating competitive talking head videos with significantly reduced runtime.
  arXiv  Detail & Related papers  (2025-08-05T13:57:03Z)
• FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers [63.788600404496115]
  FullDiT2 is an efficient in-context conditioning framework for general controllability in both video generation and editing tasks.<n>FullDiT2 achieves significant computation reduction and 2-3 times speedup in averaged time cost per diffusion step.
  arXiv  Detail & Related papers  (2025-06-04T17:57:09Z)
• BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression [12.60355288519781]
  We propose BiECVC, a learned bidirectional video compression (BVC) framework that incorporates diversified local and non-local context modeling.<n>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.<n>To our knowledge BiECVC is the first learned video to surpass VTM 13.2 across all standard test datasets.
  arXiv  Detail & Related papers  (2025-05-14T06:55:37Z)
• Augmented Deep Contexts for Spatially Embedded Video Coding [8.213635577747638]
  Most Neural Video Codecs (NVCs) only employ temporal references to generate temporal-only contexts and latent prior.<n>We propose a Spatially Embedded Video Codec (SEVC) in which the low-resolution video is compressed for spatial references.<n>Our SEVC effectively alleviates the limitations in handling large motions or emerging objects, and also reduces 11.9% more than the previous state-of-the-art NVC.
  arXiv  Detail & Related papers  (2025-05-08T14:57:52Z)
• Boosting Neural Representations for Videos with a Conditional Decoder [28.073607937396552]
  Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing. This paper introduces a universal boosting framework for current implicit video representation approaches.
  arXiv  Detail & Related papers  (2024-02-28T08:32:19Z)
• IBVC: Interpolation-driven B-frame Video Compression [68.18440522300536]
  B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. Previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation. We propose a simple yet effective structure called Interpolation-B-frame Video Compression (IBVC) to address these issues.
  arXiv  Detail & Related papers  (2023-09-25T02:45:51Z)
• Neural Video Compression with Diverse Contexts [25.96187914295921]
  This paper proposes increasing the context diversity in both temporal and spatial dimensions. Experiments show that our codes obtains 23.5% saving over previous SOTA NVC.
  arXiv  Detail & Related papers  (2023-02-28T08:35:50Z)
• Learned Video Compression via Heterogeneous Deformable Compensation Network [78.72508633457392]
  We propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance. More specifically, the proposed algorithm extracts features from the two adjacent frames to estimate content-Neighborhood heterogeneous deformable (HetDeform) kernel offsets. Experimental results indicate that HDCVC achieves superior performance than the recent state-of-the-art learned video compression approaches.
  arXiv  Detail & Related papers  (2022-07-11T02:31:31Z)
• Perceptual Learned Video Compression with Recurrent Conditional GAN [158.0726042755]
  We propose a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional generative adversarial network. PLVC learns to compress video towards good perceptual quality at low bit-rate. The user study further validates the outstanding perceptual performance of PLVC in comparison with the latest learned video compression approaches.
  arXiv  Detail & Related papers  (2021-09-07T13:36:57Z)
• Intrinsic Temporal Regularization for High-resolution Human Video Synthesis [59.54483950973432]
  temporal consistency is crucial for extending image processing pipelines to the video domain. We propose an effective intrinsic temporal regularization scheme, where an intrinsic confidence map is estimated via the frame generator to regulate motion estimation. We apply our intrinsic temporal regulation to single-image generator, leading to a powerful " INTERnet" capable of generating $512times512$ resolution human action videos.
  arXiv  Detail & Related papers  (2020-12-11T05:29:45Z)
• Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model [45.46660511313426]
  We propose an end-to-end deep neural video coding framework (NVC) It uses variational autoencoders (VAEs) with joint spatial and temporal prior aggregation (PA) to exploit the correlations in intra-frame pixels, inter-frame motions and inter-frame compensation residuals. NVC is evaluated for the low-delay causal settings and compared with H.265/HEVC, H.264/AVC and the other learnt video compression methods.
  arXiv  Detail & Related papers  (2020-07-09T06:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.