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A machine learning regression scheme to design a Fr-Image quality assessment algorithm

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Titel A machine learning regression scheme to design a Fr-Image quality assessment algorithm
Serientitel The Sixth European Conference on Colour in Graphics, Imaging, and Vision (CGIV 2012)
Teil 09
Anzahl der Teile 31
Autor Charrier, Christophe
Lezoray, Olivier
Lebrun, Gilles
Lizenz CC-Namensnennung - keine Bearbeitung 2.0 UK: England & Wales:
Sie dürfen das Werk in unveränderter Form zu jedem legalen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/31444
Herausgeber River Valley TV
Erscheinungsjahr 2012
Sprache Englisch

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Fachgebiet Informatik
Abstract A crucial step in image compression is the evaluation of its performance, and more precisely available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect that of human observers. The proposed method namely Machine Learning-based Image Quality Measurment (MLIQM) first classifies the quality using multi Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FRIQA) algorithms to judge the efficiency of the proposed method.

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