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

• URL: http://arxiv.org/abs/2504.12899v1
• Date: Thu, 17 Apr 2025 12:40:33 GMT
• Title: Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding
• Authors: Jiancheng Zhao, Yifan Zhan, Qingtian Zhu, Mingze Ma, Muyao Niu, Zunian Wan, Xiang Ji, Yinqiang Zheng,
• Abstract summary: Implicit Neural Representations for Videos (NeRV) have emerged as a powerful paradigm for video representation.<n>Existing NeRV-based methods rely on uniform sampling along the temporal axis, leading to suboptimal rate-distortion (RD) performance.<n>We propose Tree-NeRV, a novel tree-structured feature representation for efficient and adaptive video encoding.
• Score: 26.638854682076733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CANeRV: Content Adaptive Neural Representation for Video Compression [89.35616046528624]
  We propose Content Adaptive Neural Representation for Video Compression (CANeRV)<n>CANeRV is an innovative INR-based video compression network that adaptively conducts structure optimisation based on the specific content of each video sequence.<n>We show that CANeRV can outperform both H.266/VVC and state-of-the-art INR-based video compression techniques across diverse video datasets.
  arXiv  Detail & Related papers  (2025-02-10T06:21:16Z)
• DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes [81.56206845824572]
  Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. Few-shot methods often struggle with poor reconstruction quality in vast environments. This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes.
  arXiv  Detail & Related papers  (2024-11-19T07:51:44Z)
• PNeRV: A Polynomial Neural Representation for Videos [28.302862266270093]
  Extracting Implicit Neural Representations on video poses unique challenges due to the additional temporal dimension. We introduce Polynomial Neural Representation for Videos (PNeRV) PNeRV mitigates challenges posed by video data in the realm of INRs but opens new avenues for advanced video processing and analysis.
  arXiv  Detail & Related papers  (2024-06-27T16:15:22Z)
• D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video [53.83936023443193]
  This paper contributes to the field by introducing a new synthesis method for dynamic novel view from monocular video, such as smartphone captures.<n>Our approach represents the as a $textitdynamic neural point cloud$, an implicit time-conditioned point cloud that encodes local geometry and appearance in separate hash-encoded neural feature grids.
  arXiv  Detail & Related papers  (2024-06-14T14:35:44Z)
• VQ-NeRV: A Vector Quantized Neural Representation for Videos [3.6662666629446043]
  Implicit neural representations (INR) excel in encoding videos within neural networks, showcasing promise in computer vision tasks like video compression and denoising. We introduce an advanced U-shaped architecture, Vector Quantized-NeRV (VQ-NeRV), which integrates a novel component--the VQ-NeRV Block. This block incorporates a codebook mechanism to discretize the network's shallow residual features and inter-frame residual information effectively.
  arXiv  Detail & Related papers  (2024-03-19T03:19:07Z)
• Boosting Neural Representations for Videos with a Conditional Decoder [28.073607937396552]
  Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing. This paper introduces a universal boosting framework for current implicit video representation approaches.
  arXiv  Detail & Related papers  (2024-02-28T08:32:19Z)
• VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision [59.632286735304156]
  It is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. We propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis.
  arXiv  Detail & Related papers  (2023-06-19T03:04:57Z)
• Modality-Agnostic Variational Compression of Implicit Neural Representations [96.35492043867104]
  We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR) Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression.
  arXiv  Detail & Related papers  (2023-01-23T15:22:42Z)
• FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos [5.958701846880935]
  We propose FFNeRV, a novel method for incorporating flow information into frame-wise representations to exploit the temporal redundancy across the frames in videos. With model compression techniques, FFNeRV outperforms widely-used standard video codecs (H.264 and HEVC) and performs on par with state-of-the-art video compression algorithms.
  arXiv  Detail & Related papers  (2022-12-23T12:51:42Z)
• E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context [14.549945320069892]
  We propose E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context. We experimentally find that our method can improve the performance to a large extent with fewer parameters, resulting in a more than $8times$ faster speed on convergence.
  arXiv  Detail & Related papers  (2022-07-17T10:16:47Z)
• Neural BRDF Representation and Importance Sampling [79.84316447473873]
  We present a compact neural network-based representation of reflectance BRDF data. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real-world datasets.
  arXiv  Detail & Related papers  (2021-02-11T12:00:24Z)

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.