Recently, the image-wise implicit neural representation of videos, NeRV, has
gained popularity for its promising results and swift speed compared to regular
pixel-wise implicit representations. However, the redundant parameters within
the network structure can cause a large model size when scaling up for
desirable performance. The key reason of this phenomenon is the coupled
formulation of NeRV, which outputs the spatial and temporal information of
video frames directly from the frame index input. In this paper, we propose
E-NeRV, which dramatically expedites NeRV by decomposing the image-wise
implicit neural representation into separate spatial and temporal context.
Under the guidance of this new formulation, our model greatly reduces the
redundant model parameters, while retaining the representation ability. We
experimentally find that our method can improve the performance to a large
extent with fewer parameters, resulting in a more than $8\times$ faster speed
on convergence. Code is available at this https URL

本文提出了一种名为 E-NeRV 的新模型，通过将图像隐式神经表示分解为独立的空间和时间上下文，极大地减少了模型中的冗余参数，同时保留了 NeRV 的表达能力，并使性能得到显著提高。