Abstract
In this paper, we proposed a new measure to solve the full reference image quality assessment problem. The core of the approach is known as peak signal-to-noise ratio improved with the estimation of local block-wise distortions, contrast, and saturation differences between test and referenced images. A measure that includes these values into a common quality score has been proposed. The proposed measure includes factors and thresholds which allow tuning the measure according to the specific features of the particular image dataset. The iterative numerical partial optimization algorithm for the fine-tuning of these factors and thresholds has been proposed, implemented, and tested as well as start optimization point selection has been described. The dependency of quality measure on parameters fine-tuning has been investigated. The usage of the proposed quality metric for the processing of TID2013 and CSIQ datasets as well as its computational complexity has been investigated. The results of modeling have been shown that it is possible to build the image quality measure in a fraction of a second preserving the average comparison quality in terms of the mean opinion score provided by humans.
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Gorokhovatskyi, O., Peredrii, O. (2022). Fine-Tuning of the Measure for Full Reference Image Quality Assessment. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_29
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