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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.
Chang et al. (Tue,) studied this question.