A variety of compression methods based on encoding images as weights of a neural network have been recently proposed. Yet, the potential of similar approaches for video compression remains unexplored. In this work, we suggest a set of experiments for testing the feasibility of compressing video using two architectural paradigms, coordinate-based MLP (CbMLP) and convolutional network. Furthermore, we propose a novel technique of neural weight stepping, where subsequent frames of a video are encoded as low-entropy parameter updates. To assess the feasibility of the considered approaches, we will test the video compression performance on several high-resolution video datasets and compare against existing conventional and neural compression techniques.

翻译：最近提出了基于神经网络重量的编码图像的各种压缩方法。然而,类似的视频压缩方法的潜力尚未探索。在这项工作中,我们建议进行一系列实验,用两个建筑范式、基于协调的MLP(CbMLP)和进化网络来测试压缩视频的可行性。此外,我们提出了一种新的神经重量阶梯技术,随后的视频框架被编码为低精度参数更新。为了评估经过考虑的方法的可行性,我们将测试若干高分辨率视频数据集的视频压缩性能,并与现有的常规和神经压缩技术进行比较。