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English(EN) ResGVC: Residual Coding for High-fidelity Generative Video Compression

ResGVC框架通过残差编码增强生成式视频压缩

研究人员推出了一种新颖的生成式视频压缩框架ResGVC,该框架通过传输残差信号来增强保真度。该方法通过在每个去噪步骤中对残差片段进行编码,引导去噪过程趋向于原始图像。该框架利用扩散Transformer作为这些残差片段的熵模型,从而实现更有效的分布建模。实验表明,与最先进的方法相比,在LPIPS和PSNR方面显著降低了BD率,同时提高了视觉质量。 AI

影响 这项研究可能带来更高效的视频压缩技术,影响流媒体服务和视频存储。

排序理由 该集群包含一篇详细介绍视频压缩新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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ResGVC框架通过残差编码增强生成式视频压缩

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该集群包含一篇详细介绍视频压缩新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ding Ding, Daowen Li, Ying Chen, Kai Li ·

    ResGVC: Residual Coding for High-fidelity Generative Video Compression

    arXiv:2610.31039v1 Announce Type: new Abstract: Generative video compression (GVC) methods usually employ decoder-side diffusion models to denoise reconstructed signals based on generative priors. Besides, most of these methods perform denoising in latent space rather than direct…