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arXiv · 2504.12899

Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding

Jiancheng Zhao·Yifan Zhan·Qingtian Zhu·Mingze Ma·Muyao Niu·Zunian Wan·Xiang Ji·Yinqiang Zheng
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Abstract

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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Jiancheng Zhao, Yifan Zhan, Qingtian Zhu, Mingze Ma, Muyao Niu, Zunian Wan, Xiang Ji, Yinqiang Zheng. 2025-04-17. Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding. https://arxiv.org/abs/2504.12899

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