Class-count reduction techniques for content adaptive filtering

In the field of image/video enhancement, content adaptive filtering has shown superior performance over fixed linear filtering. The content adaptive filtering, first classifies the local image content based on different image features, such as structure and contrast. Then in every class, a least mean square (LMS) optimal filter is applied. A disadvantage of the concept is that many classes may be redundant, which leads to an inefficient implementation. In this paper, we propose and evaluate various class-count reduction techniques based on class-occurrence frequency, coefficient similarity and error advantage, which can greatly simplify the implementation without sacrificing much performance.