Optimizing video signal processing algorithms by evolution strategies

Today many kinds of postprocessing are used in digital TV receivers or multimedia terminals for video signals to enhance the picture quality. To achieve this the properties of human visual perception have to be regarded. Because of the nonlinear nature of human visual perception (e.g. perception of edges and objects) many algorithms have been developed and optimized by heuristic methods or by application of rough image models. This is a severe problem as there are sometimes contradictory demands (e.g. detail resolution and alias suppression) and there are many optimization problems which cannot be solved analytically. Furthermore the simulations which have to be carried out in the field of video processing have to take into account a great variety of test sequences and therefore possess a heavy simulation load. In this paper we present the results of evolution strategies (ES) applied to develop and optimize some modules of a video signal processing feature box. The modules we have analyzed are as follows: A proscan conversion module is required to convert incoming interlaced TV signals into a progressive format which is obligatory for computer monitors or LCD and DMD devices (e.g. projectors) as they cannot display interlaced signals [1]. Further linear and nonlinear filter techniques are required for spatial conversion techniques like zooming or a picture in picture reproduction. For high quality temporal scan conversion techniques (e.g. 50 Hz interlace to 100 Hz interlace scan conversion reducing annoying artifacts as large area or detail flicker) motion vector based video processing is state of the art [1]. The motion information is generated by motion estimation algorithms.