Machine learning to design full-reference image quality assessment algorithm

doi:10.1016/j.image.2012.01.002

Machine learning to design full-reference image quality assessment algorithm

2012-03-01

37

PRE

AI

C

Christophe Charrier *

DOI：10.1016/j.image.2012.01.002

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En 中文

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 human observers. The proposed method namely Machine Learning-based Image Quality Measure (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 (FR-IQA) algorithms to judge the efficiency of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.

Keyword:

FR-IQA algorithm

Classification

Theory of evidence

SVM classification

SVM regression

Machine learning to design full-reference image quality assessment algorithm

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