MNeRV：一种用于视频的多层神经表示方法
MNeRV: A Multilayer Neural Representation for Videos
AI总结：
针对现有视频神经表示NeRV解码层数少导致参数冗余、限制回归能力的问题，提出多层神经表示MNeRV及配套编解码器与模块，以更少参数实现更优重建质量，并在下游任务中展现良好性能。
摘要：
作为一种新颖的视频表示方法，视频神经表示（NeRV）已在视频压缩、视频修复和视频插帧领域展现出巨大潜力。在使用NeRV表示视频的过程中，每一帧对应一个嵌入向量，该嵌入向量经过少量解码层（如E-NeRV、HNeRV等）后被重建为视频帧序列。然而，由于单个解码层的参数占比过大，这种少量解码层的结构容易导致模型参数冗余的问题，极大地限制了神经网络模型的视频回归能力。本文提出了一种用于视频的多层神经表示方法MNeRV，并设计了新型解码器M-Decoder及其配套的编码器M-Encoder。MNeRV拥有更多的编码层与解码层，有效缓解了层数过少导致的模型参数冗余问题。此外，我们设计了MNeRV块，以在各解码层之间实现更均匀、更有效的参数分配。在视频回归重建领域，我们以更少的参数实现了更优的重建质量（PSNR提升4.06）。最后，我们展示了MNeRV在视频修复、视频插帧等下游任务中的性能。MNeRV的源代码可在https://github.com/Aaronbtb/MNeRV 获取。
英文摘要：
As a novel video representation method, Neural Representations for Videos (NeRV) has shown great potential in the fields of video compression, video restoration, and video interpolation. In the process of representing videos using NeRV, each frame corresponds to an embedding, which is then reconstructed into a video frame sequence after passing through a small number of decoding layers (E-NeRV, HNeRV, etc.). However, this small number of decoding layers can easily lead to the problem of redundant model parameters due to the large proportion of parameters in a single decoding layer, which greatly restricts the video regression ability of neural network models. In this paper, we propose a multilayer neural representation for videos (MNeRV) and design a new decoder M-Decoder and its matching encoder M-Encoder. MNeRV has more encoding and decoding layers, which effectively alleviates the problem of redundant model parameters caused by too few layers. In addition, we design MNeRV blocks to perform more uniform and effective parameter allocation between decoding layers. In the field of video regression reconstruction, we achieve better reconstruction quality (+4.06 PSNR) with fewer parameters. Finally, we showcase MNeRV performance in downstream tasks such as video restoration and video interpolation. The source code of MNeRV is available at https://github.com/Aaronbtb/MNeRV.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
