Automated video chain optimization

Video processing algorithms found in complex video appliances such as television sets and set top boxes exhibit an interdependency that makes it is difficult to predict the picture quality of an end product before it is actually built. This quality is likely to improve when algorithm interaction is explicitly considered. Moreover, video algorithms tend to have many programmable parameters, which are traditionally tuned in manual fashion. Tuning these parameters automatically rather than manually is likely to speed up product development. We present a methodology that addresses these issues by means of a genetic algorithm that, driven by a novel objective image quality metric, finds high-quality configurations of the video processing chain of complex video products.