Evaluation of machine learning algorithms for fast video transcoding in streaming services

Abstract

With the recent rise in artificial intelligence, several areas of computer science have begun to explore the use of machine learning algorithms. Among them, recent video encoding techniques exploit in large-scale machine learning algorithms, either to aid in compression efficiency or to reduce computational cost. However, this is not a simple task, especially if the objective is to reduce computational cost, since it is necessary that the machine learning algorithm used does not interfere negatively in the total coding process time and in compression efficiency. As video transrating is an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users operating under different network conditions, it is critical to find an efficient strategy that yields computational cost reduction with small loss in compression efficiency. This work discusses the use of different machine learning algorithms in the video transcoding concept, comparing them in terms of accuracy and overhead processing time. The experimental results and discussions provide insights that can be useful in the definition of future fast transcoding schemes in real-world transcoding architectures.