A Machine Learning based Framework for Parameter based Multi-Objective Optimisation of Video CODECs
Advances in Science, Technology and Engineering Systems
All multimedia devices now incorporate video CODECs that comply with international video coding standards such as H.264 / MPEG4-AVC and the new High Efficiency Video Coding Standard (HEVC), otherwise known as H.265. Although the standard CODECs have been designed to include algorithms with optimal efficiency, a large number of coding parameters can be used to fine-tune their operation, within known constraints of for example, available computational power, bandwidth, energy consumption, etc.
... h the large number of such parameters involved, determining which parameters will play a significant role in providing optimal quality of service within given constraints is a further challenge that needs to be met. We propose a framework that uses machine learning algorithms to model the performance of a video CODEC based on the significant coding parameters. We define objective functions that can be used to model the video quality as Peak Signal-to-Noise Ratio (PSNR), CPU time utilization and Bit-Rate. We show that these objective functions can be practically utilised in video Encoder designs, in particular in their performance optimisation within given constraints. A Multi-objective Optimisation framework based on Genetic Algorithms is thus proposed to optimise the performance of a video codec. The framework is designed to jointly minimize the complexity, Bit-rate and to maximize the quality of the compressed video stream.