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.

通过引入条件解码器与熵最小化技术，该研究提出了一种通用增强框架，成功提升了隐式神经表示在视频存储与处理中的效果，并在多个基线模型上展现了优越的重建质量和收敛速度，为视频回归、修复和插值任务提供了一种强大的技术。