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Class-count reduction techniques for content adaptive filtering.

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IEEE
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Class-count Reduction Techniques for Content Adaptive Filtering Class-count Reduction Techniques for Content Adaptive Filtering Hao Hu Eindhoven University of Technology Eindhoven, the Netherlands Email: [email protected] Gerard de Haan Philips Research Europe Eindhoven, the Netherlands Abstract—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. I. INTRODUCTION Image/video enhancement often involves filtering. Content adaptive filtering has received considerable attention recently due to its superior performance over fixed filters [1]. A content adaptive filter first classifies local image content based on different image features, such as structures and contrast. Then in every class, a least mean square (LMS) optimal filter is employed. The structure adaptive filter was first proposed for image interpolation by Kondo [2], where only the image struc- ture such as edge direction or luminance pattern is used for the classification. In applications to coding artifact reduction, additional features like block grid position [4], local contrast [5] and local variance [6], have been utilized. Incorporating more features in the classification improves the performance of the content adaptive filters, but also leads to an explosion of the class-count, many of which may be redundant. For hardware implementation, a class-count reduction tech- nique that allow

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