A Novel Framework for Multi-objective Optimization of Video CODECs

Abstract

In this paper we propose a novel framework for the multi-objective optimization of a video CODEC based on genetic algorithms. The proposed framework is designed to jointly minimize the complexity, memory usage (both at the encoder and decoder), bit rate and to maximize the quality of the compressed video stream. In particular, in our present attempt the optimization strategy is designed to determine the optimum coding parameters for a H.264 AVC video codec in a memory and bandwidth constrained environment. This is demonstrated through extensive experiments and mathematical formulation that results in the optimum solution/s to the multi-objective optimisation problem being found. We show that such an approach is highly desirable in obtaining optimum coding parameters for video delivery over the internet, where a feedback channel from the decoder to the encoder is practical. .

A Machine Learning based Framework for Parameter based Multi-Objective Optimisation of Video CODECs

“…The author in [9] presented a detailed study of the importance of a multi-objective optimisation framework and the approach presented in [10] as a solution. The presented framework focused on the development of a joint complexity-rate distortion (C-M-R-D) optimization framework for a H.264 video CODEC, which could be extended to cover any number of constraints and to be used within any type of video CODEC.…”