An Overview and Performance Evaluation of Classification-Based Least Squares Trained Filters

• Published in: IEEE transactions on image processing Vol. 17; no. 10; pp. 1772 - 1782
• Main Authors: Ling Shao, Hui Zhang, de Haan, G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Algorithm design and analysis; Algorithms; Applied sciences; Artificial Intelligence; Benchmarking; Cathode ray tubes; Classification; Computer Simulation; Degradation; Design optimization; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Frequency; Frequency spectrum; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Information, signal and communications theory; integrated processing; Least squares method; Least squares methods; least squares optimization; Least-Squares Analysis; Liquid crystal displays; Marketing; Models, Statistical; Noise reduction; Optimization; Pattern Recognition, Automated - methods; performance evaluation; Plasma displays; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Subtraction Technique; Telecommunications and information theory; trained filters; video enhancement; Performance evaluation; trained filters; Image processing; video enhancement; Objective analysis; Adaptive filters; least squares optimization; Review; Adaptive filter; classification; Algorithm; Optimization; Video signal processing; integrated processing; Learning; Image quality; Least squares method; Integrated design; Frequency spectrum; Image enhancement; Cascade connection
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.2002162

An overview of the classification-based least squares trained filters on picture quality improvement algorithms is presented. For each algorithm, the training process is unique and individually selected classification methods are proposed. Objective evaluation is carried out to single out the optimal classification method for each application. To optimize combined video processing algorithms, integrated solutions are benchmarked against cascaded filters. The results show that the performance of integrated designs is superior to that of cascaded filters when the combined applications have conflicting demands in the frequency spectrum.