Neural Video Compression with Domain Transfer

• URL: http://arxiv.org/abs/2605.13476v1
• Date: Wed, 13 May 2026 13:02:40 GMT
• Title: Neural Video Compression with Domain Transfer
• Abstract summary: DCVC-DT is a domain transfer enhanced neural video compression framework.<n>We develop a frame-level dynamic RD (Rate and Distortion) adjustment scheme that actively regulates the ratio of R and D in the loss based on quality fluctuation.
• Score: 67.17233008935996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Content-adaptive compression has always been a key direction in neural video coding (NVC), aiming to mitigate the domain gap between training and testing data. Such gaps often arise from distributional discrepancies between training and inference data, which may cause noticeable performance degradation when the testing content differs from the training distribution. To tackle this challenge, we propose DCVC-DT, a domain transfer enhanced neural video compression framework. Specifically, we design a lightweight online domain transfer (DT) mechanism that dynamically adapts the encoded latent representation during inference, effectively bridging the domain gap without modifying the encoder or decoder parameters. In addition, we develop a frame-level dynamic RD (Rate and Distortion) adjustment scheme that actively regulates the ratio of R and D in the loss function based on quality fluctuation, thereby improving rate-distortion performance. Extensive experiments demonstrate that DCVC-DT achieves up to 6.21% bitrate savings over the baseline DCVC-DC, while significantly enhancing generalization to unseen testing data and alleviating error propagation. Our code is available at https://github.com/SunnyMass/DCVC-DT.

Related papers

• Boosting Neural Video Codec via Scale-Driven Online Flow Refinement [59.68390983151205]
  We propose a Training-Free Scale-Driven Online Flow Refinement (SOFR) method.<n>It fuses motion information from coarse and fine scales according to warping accuracy, effectively rectifying motion estimation errors.<n>It brings an average of 2.84% and 4.05% savings in terms of PSNR and MS-SSIM, respectively.
  arXiv  Detail & Related papers  (2026-06-22T08:36:23Z)
• Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noise [51.028614105626154]
  Static scene videos, such as surveillance feeds and videotelephony streams, constitute a dominant share of storage consumption and network traffic.<n>Traditional standardized codecs and neural video compression (NVC) methods struggle to encode these videos efficiently due to inadequate usage of temporal redundancy and severe distribution gaps between training and test data.<n>We propose to incorporate positive-incentive noise into NVC for static scene videos, where short-term temporal changes are reinterpreted as positive-incentive noise to facilitate model finetuning.
  arXiv  Detail & Related papers  (2026-03-06T09:47:25Z)
• SCALED : Surrogate-gradient for Codec-Aware Learning of Downsampling in ABR Streaming [9.436544348188598]
  Over-the-Top (OTT) delivery now predominantly relies on Adaptive Bitrate (ABR) streaming.<n>Deep learning has spurred interest in jointly optimizing the ABR pipeline using learned resampling methods.<n>We introduce a novel framework that enables end-to-end training with real, non-differentiable codecs.
  arXiv  Detail & Related papers  (2026-01-30T10:38:35Z)
• Content Adaptive based Motion Alignment Framework for Learned Video Compression [72.13599533975413]
  This paper proposes a content adaptive based motion alignment framework.<n>We first introduce a two-stage flow-guided deformable warping mechanism that refines motion compensation with coarse-to-fine offset prediction and mask modulation.<n>Second, we propose a multi-reference quality aware strategy that adjusts distortion weights based on reference quality, and applies it to hierarchical training to reduce error propagation.<n>Third, we integrate a training-free module that downsamples frames by motion magnitude and resolution to obtain smooth motion estimation.
  arXiv  Detail & Related papers  (2025-12-15T02:51:47Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• Learned Rate Control for Frame-Level Adaptive Neural Video Compression via Dynamic Neural Network [8.645355715511702]
  We propose a dynamic video compression framework designed for variable scenarios.<n>The proposed method achieves an average BD-Rate reduction of 14.8% and BD-PSNR gain of 0.47dB over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-28T12:27:23Z)
• Rethinking Video Tokenization: A Conditioned Diffusion-based Approach [58.164354605550194]
  New tokenizer, Diffusion Conditioned-based Gene Tokenizer, replaces GAN-based decoder with conditional diffusion model.<n>We trained using only a basic MSE diffusion loss for reconstruction, along with KL term and LPIPS perceptual loss from scratch.<n>Even a scaled-down version of CDT (3$times inference speedup) still performs comparably with top baselines.
  arXiv  Detail & Related papers  (2025-03-05T17:59:19Z)
• VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression [59.14355576912495]
  NeRF-based video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences.<n>The substantial data volumes pose significant challenges for storage and transmission.<n>We propose VRVVC, a novel end-to-end joint variable-rate framework for video compression.
  arXiv  Detail & Related papers  (2024-12-16T01:28:04Z)
• Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression [33.92792778925365]
  We propose a low-rank adaptation approach to address the rate-distortion drop observed in out-of-domain datasets. Our proposed method exhibits universality across diverse image datasets.
  arXiv  Detail & Related papers  (2023-08-15T12:17:46Z)
• OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement [24.91807723834651]
  Convolution neural network (CNN) based methods offer effective solutions for enhancing the quality of compressed image and video. In this paper, we adopt the raw data in the quality enhancement for the HEVC intra-coded image by proposing an online learning-based method. Our proposed online learning based dual-domain network (OL-DN) has achieved superior performance, compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-08-09T11:06:59Z)

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.