Automated qualitative assessment of multi-modal distortions in digital images based on GLZ
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This paper introduces a novel approach to a qualitative assessment of images affected by multi-modal distortions. The idea is to assess the image quality perceived by an end user in an automatic way in order to avoid the usual time-consuming, costly and non-repeatable method of collecting subjective scores during a psycho-physical experiment. This is achieved by computing quantitative image distortions and mapping results on qualitative scores. Useful mapping models have been proposed and constructed using the generalised linear model (GLZ), which is a generalisation of the least squares regression in statistics for ordinal data. Overall qualitative image distortion is computed based on partial quantitative distortions from component algorithms operating on specified image features. Seven such algorithms are applied to successfully analyse the seven image distortions in relation to the original image. A survey of over 12,000 subjective quality scores has been carried out in order to determine the influence of these features on the perceived image quality. The results of quantitative assessments are mapped on the surveyed scores to obtain an overall quality score of the image. The proposed models have been validated in order to prove that the above technique can be applied to automatic image quality assessment.
KeywordsImage quality Image distortion MOS Mean opinion score GLZ Generalised linear models Quality metrics
The work presented in this paper was supported in part by Telekomunikacja Polska S.A. and the grants funded by EC (CONTENT FP6-0384239, INDECT FP7-218086) and Polish MNiSW (PBZ-MNiSW-02/II/2007 and N 517 4388 33).
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