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。

HiNeRV：基于分层编码的神经表达的视频压缩

HiNeRV: Video Compression with Hierarchical Encoding based Neural  Representation

One key challenge to learning-based video compression is that motion
predictive coding, a very effective tool for video compression, can hardly be
trained into a neural network. In this paper we propose the concept of
PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction
networks. PMCNN can model spatiotemporal coherence to effectively perform
predictive coding inside the learning network. On the basis of PMCNN, we
further explore a learning-based framework for video compression with
additional components of iterative analysis/synthesis, binarization, etc.
Experimental results demonstrate the effectiveness of the proposed scheme.
Although entropy coding and complex configurations are not employed in this
paper, we still demonstrate superior performance compared with MPEG-2 and
achieve comparable results with H.264 codec. The proposed learning-based scheme
provides a possible new direction to further improve compression efficiency and
functionalities of future video coding.

本文提出了 PixelMotionCNN 的概念，并采用基于学习的框架进行视频压缩，该框架包括迭代分析 / 综合，二值化等组件，实验结果表明该方案与 H.264 编解码器具有相当的性能表现，为未来视频编码的进一步提高压缩效率和    功能提供了可能的新方向。