Video color enhancement using neural networks

Television pictures are not always at their best color saturation settings. We propose an intelligent system that automatically adjusts the color saturation on a field-by-field basis. Incoming pictures are first classified into one of two categories, namely, facial tone and nonfacial tone images, then each category is subcategorized. Depending on the membership of the picture to each subcategory, color saturation changes are computed and used to modify the color saturation of the following field. The categorization is done by using neural network classifiers operating on the color difference signals. We describe the principles of the color correction scheme, its implementation using analog hardware and its interface to a television. Performance of the color adjustment technique implemented using real-time hardware in a television is discussed. Oversaturated and under-saturated pictures in both facial and nonfacial tone categories can be detected and corrected. The technique lends itself to low-cost analog implementations