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English(EN) LRConv-NeRV: Low Rank Convolution for Efficient Neural Video Compression

新研究推动神经视频压缩技术发展 · 2篇论文

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文提出了神经视频压缩的改进方法。第一篇论文介绍了一种结合可变形时间对齐和差分感知空间选择性融合的方法，以提高上下文质量并减少由运动、遮挡和纹理引起的错误。第二篇论文提出了LRConv-NeRV，它使用低秩可分离卷积在不显著损失质量的情况下，显著降低神经视频表示的计算复杂度和模型大小。 AI

影响 神经视频压缩的这些进步可能带来更高效的视频流和存储解决方案。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了神经视频压缩的新方法。

在 arXiv cs.AI 阅读 →

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新研究推动神经视频压缩技术发展 · 2篇论文

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Research
两篇在arXiv上发表的学术论文，详细介绍了神经视频压缩的新方法。
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2 independent sources
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Chuyue Shan, Songlin Sun, Wang Chenwei, Shen Zihan ·

    基于可变形时间对齐和差分感知融合的神经视频压缩

    arXiv:2609.03520v1 Announce Type: cross Abstract: In conditional coding-based neural video compression, the quality of temporal context directly affects compression per- formance. Existing methods mostly construct context from prop- agated reference features, but they are vulnera…

  2. arXiv cs.AI TIER_1 English(EN) · Tamer Shanableh ·

    LRConv-NeRV：低秩卷积实现高效神经视频压缩

    arXiv:2603.18261v2 Announce Type: replace-cross Abstract: Neural Representations for Videos (NeRV) encode entire video sequences within neural network parameters, offering an alternative paradigm to conventional video codecs. However, the convolutional decoder of NeRV remains com…

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