8 February 2015 Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images
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Abstract
No-reference image quality metrics are of fundamental interest as they can be embedded in practical applications. The main goal of this paper is to perform a comparative study of seven well known no-reference learning-based image quality algorithms. To test the performance of these algorithms, three public databases are used. As a first step, the trial algorithms are compared when no new learning is performed. The second step investigates how the training set influences the results. The Spearman Rank Ordered Correlation Coefficient (SROCC) is utilized to measure and compare the performance. In addition, an hypothesis test is conducted to evaluate the statistical significance of performance of each tested algorithm.
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Christophe Charrier, AbdelHakim Saadane, Christine Fernandez-Maloigne, "Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images", Proc. SPIE 9396, Image Quality and System Performance XII, 939610 (8 February 2015); doi: 10.1117/12.2076145; https://doi.org/10.1117/12.2076145
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