Parameter Based Multi-Objective Optimization of Video CODECs

Parameter Based Multi-Objective Optimization of Video CODECs

F. Al-Abri (Loughborough University, UK), E.A. Edirisinghe (Loughborough University, UK) and C. Grecos (University of the West of Scotland, UK)
DOI: 10.4018/978-1-60960-477-6.ch017
OnDemand PDF Download:
No Current Special Offers


This chapter presents a generalised framework for multi-objective optimisation of video CODECs for use in off-line, on-demand applications. In particular, an optimization scheme is proposed to determine the optimum coding parameters for a H.264 AVC video codec in a memory and bandwidth constrained environment, which minimises codec complexity and video distortion. The encoding/decoding parameters that have a significant impact on the performance of the codec are initially obtained through experimental analysis. A mathematical formulation by means of regression is subsequently used to associate these parameters with the relevant objectives and define a Multi-Objective Optimization (MOO) problem. Solutions to the optimization problem are reached through a Non-dominated Sorting Genetic Algorithm (NSGA-II). It is shown that the proposed framework is flexible on the number of objectives that can jointly be optimized. Furthermore, any of the objectives can be included as constraints depending on the requirements of the services to be supported. Practical use of the proposed framework is described using a case study that involves video content transmission to a mobile hand.
Chapter Preview


Recently a significant amount of research effort has been focused on optimizing video CODECs, especially within the standardization activities of MPEG/JVT. This is due to the high demand of applications requiring efficient on-demand and real-time video coding, supported by the effective usage of capture, processing and display devices and transmissions mediums, which are practically operational under varying constraints.

To this date many optimization methods have been proposed in literature. They can be broadly classified into two categories, namely,

  • Algorithm-based optimizations

  • Parameter-based optimizations

The algorithm-based optimization methods focus on the direct performance optimization of a given algorithm. Alternatively, parameter-based optimization methods optimize given objectives through the optimal selection of coding parameters. Whilst the first approach focuses on the optimisation of a CODEC when the algorithms are being developed, the latter enables the optimal operation of a standardized algorithm by the selection of optimal sets of parameters.

With the standardization of H.264, the video optimization research has mainly been focused on this standard. Due to the vast amount of effort that has been put into the algorithmic optimization of H.264 during it’s international standardization, the need at present is a unified framework that is capable of selecting the vast number of coding parameters one can set and specify to obtain the CODEC’s optimum performance under given constraints. Therefore the focus of optimisation subsequent to the standardization activity of any video CODEC is parameter based optimisation.

This chapter presents a multi-objective optimisation strategy for the parameter-based optimisation of H.264 AVC (Advanced Video Coding) standard. Amongst a detailed experimental analysis of the proposed framework the chapter provides a case study that demonstrates the practical use of the approach.

Complete Chapter List

Search this Book: