Learning-based video compression is currently one of the most popular
research topics, offering the potential to compete with conventional standard
video codecs. In this context, Implicit Neural Representations (INRs) have
previously been used to represent and compress image and video content,
demonstrating relatively high decoding speed compared to other methods.
However, existing INR-based methods have failed to deliver rate quality
performance comparable with the state of the art in video compression. This is
mainly due to the simplicity of the employed network architectures, which limit
their representation capability. In this paper, we propose HiNeRV, an INR that
combines bilinear interpolation with novel hierarchical positional encoding.
This structure employs depth-wise convolutional and MLP layers to build a deep
and wide network architecture with much higher capacity. We further build a
video codec based on HiNeRV and a refined pipeline for training, pruning and
quantization that can better preserve HiNeRV's performance during lossy model
compression. The proposed method has been evaluated on both UVG and MCL-JCV
datasets for video compression, demonstrating significant improvement over all
existing INRs baselines and competitive performance when compared to
learning-based codecs (72.3% overall bit rate saving over HNeRV and 43.4% over
DCVC on the UVG dataset, measured in PSNR).

本文介绍了一种基于 HiNeRV 的视频编解码器，该编解码器具有更高的容量和更好的性能，并通过训练、修剪和量化的精细管道来保持其在有损模型压缩中的性能。