Optical EngineeringImage quality assessment using full-parameter singular value decomposition
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A new full-parameter singular value decomposition-based image quality assessment (IQA) method, which aims at capturing the loss of structural content instead of measuring the distortion of pixel intensity value, is proposed. Both the singular vectors and the singular value are considered as features and weight for quantifying major information, respectively, to evaluate the distortion degree in images. Extensive validation experiments are conducted with two kinds of test images, one of which is the LIVE database supplied by the University of Texas and the other is created from our own simulation. The prediction performance of the presented metrics, such as accuracy, monotonicity, and consistency, is measured. The experiment results show that, compared to several state-of-the-art image quality metrics, the performance of the proposed IQA is in better alignment with the perception of the human visual system in predicting image quality, particularly when comparing images containing different types of distortions.