Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression
This work addresses the need for reduced complexity in video encoding to lower costs and environmental impact, though it appears incremental as it builds on existing neural network methods.
The paper tackled the problem of high computational complexity in neural network-based intra-prediction modes for video compression by deriving simplified models, resulting in efficient compression solutions.
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.