Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks. However, existing methods often fail to fully leverage their representation capabilities, primarily due to inadequate alignment of intermediate features during target frame decoding. This paper introduces a universal boosting framework for current implicit video representation approaches. Specifically, we utilize a conditional decoder with a temporal-aware affine transform module, which uses the frame index as a prior condition to effectively align intermediate features with target frames. Besides, we introduce a sinusoidal NeRV-like block to generate diverse intermediate features and achieve a more balanced parameter distribution, thereby enhancing the model's capacity. With a high-frequency information-preserving reconstruction loss, our approach successfully boosts multiple baseline INRs in the reconstruction quality and convergence speed for video regression, and exhibits superior inpainting and interpolation results. Further, we integrate a consistent entropy minimization technique and develop video codecs based on these boosted INRs. Experiments on the UVG dataset confirm that our enhanced codecs significantly outperform baseline INRs and offer competitive rate-distortion performance compared to traditional and learning-based codecs.

翻译：隐式神经表征（INR）已成为视频存储与处理领域一种极具前景的方法，在各类视频任务中展现出卓越的多功能性。然而，现有方法往往未能充分释放其表征能力，这主要归因于目标帧解码过程中中间特征对齐不充分。本文提出一种适用于当前隐式视频表征方法的通用增强框架。具体而言，我们采用带有时序感知仿射变换模块的条件解码器，将帧索引作为先验条件，有效实现中间特征与目标帧的对齐。此外，我们引入正弦型NeRV类模块以生成多样化的中间特征，并实现更均衡的参数分布，从而提升模型容量。结合高频信息保持的重构损失函数，该方法在视频回归任务中有效提升了多种基线INR的重构质量与收敛速度，并在修复和插值任务中展现出更优性能。进一步地，我们集成了一致性熵最小化技术，基于这些增强型INR开发了视频编解码器。UVG数据集上的实验证实，相较于基线INR，我们的增强型编解码器性能显著提升，并且在与传统及基于学习的编解码器相比时，提供了具有竞争力的率失真性能。