Repairing imperfect video enhancement algorithms using classification-based trained filters

There are numerous video processing algorithms and modules available. When the algorithms are not optimally tuned, undesired results may happen in the processed video signals, e.g. blurring, overshoots/downshoots, loss of details and aliasing. When the video processing modules are fixed, e.g. when the modules are implemented in hardware/chips, it is highly desirable to repair those unpleasant effects caused by certain imperfect algorithms. In this paper, we propose a solution based on classification and least squares trained filters to repair/patch low-quality video processing modules at the back end of a video chain. Extensive experiments show that the repairing method can significantly improve the video quality without modifying the original processing modules.