论文标题
基于神经网络基于视频压缩的基于神经网络的预测模式的分析简化
Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression
论文作者
论文摘要
随着对较高分辨率的视频内容的需求不断增长，找到限制视频编码任务复杂性以减少视频服务的成本，功耗和环境影响的方法至关重要。在过去的几年中，基于神经网络（NN）的算法已被证明使许多常规的视频编码模块受益。但是，尽管这样的技术可以大大提高压缩效率，但它们通常在计算中非常密集。简化NN学到的模型是非常有益的，以便可以利用有意义的见解，以推导不太复杂的解决方案。本文提出了两种方法来从学习模型中得出简化的预测，并表明这些简化的技术可以导致有效的压缩解决方案。
With the increasing demand for video content at higher resolutions, it is evermore critical to find ways to limit the complexity of video encoding tasks in order to reduce costs, power consumption and environmental impact of video services. In the last few years, algorithms based on Neural Networks (NN) have been shown to benefit many conventional video coding modules. But while such techniques can considerably improve the compression efficiency, they usually are very computationally intensive. It is highly beneficial to simplify models learnt by NN so that meaningful insights can be exploited with the goal of deriving less complex solutions. This paper presents two ways to derive simplified intra-prediction from learnt models, and shows that these streamlined techniques can lead to efficient compression solutions.
