用树结构实现视频非均匀采样，提升压缩效率与画质。
Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding
- 构建二叉搜索树组织视频特征，支持时间轴非均匀采样。
- 动态分配更高采样密度于变化剧烈的时段，提升编码效率。
- 适合追求高压缩比与高质量视频重建的研究者与工程师。
视频的隐式神经表示（NeRV）已成为一种强大的视频表征范式，可直接将帧索引映射为视频帧。然而，现有基于NeRV的方法未充分挖掘时间冗余，因其依赖时间轴上的均匀采样，导致率失真（RD）性能欠佳。为此，我们提出Tree-NeRV，一种用于高效自适应视频编码的树状结构特征表示。与传统方法不同，Tree-NeRV在二叉搜索树（BST）中组织特征表示，实现时间轴上的非均匀采样。此外，我们引入优化驱动的采样策略，动态将更高采样密度分配给时间变化更大的区域。大量实验表明，Tree-NeRV在压缩效率和重建质量上均优于先前基于均匀采样的方法。代码将公开。
原文摘要 · Abstract (English)
Implicit Neural Representations for Videos (NeRV) have emerged as a powerful paradigm for video representation, enabling direct mappings from frame indices to video frames. However, existing NeRV-based methods do not fully exploit temporal redundancy, as they rely on uniform sampling along the temporal axis, leading to suboptimal rate-distortion (RD) performance. To address this limitation, we propose Tree-NeRV, a novel tree-structured feature representation for efficient and adaptive video encoding. Unlike conventional approaches, Tree-NeRV organizes feature representations within a Binary Search Tree (BST), enabling non-uniform sampling along the temporal axis. Additionally, we introduce an optimization-driven sampling strategy, dynamically allocating higher sampling density to regions with greater temporal variation. Extensive experiments demonstrate that Tree-NeRV achieves superior compression efficiency and reconstruction quality, outperforming prior uniform sampling-based methods. Code will be released.
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