  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.
