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Volume: 30 | Article ID: art00013
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Advantages of Incorporating Perceptual Component Models into a Machine Learning framework for Prediction of Display Quality
  DOI :  10.2352/ISSN.2470-1173.2018.12.IQSP-299  Published OnlineJanuary 2018
Abstract

Recent work in prediction of overall HDR and WCG display quality has shown that machine learning approaches based on physical measurements performs on par with more advanced perceptually transformed measurements. While combining machine learning with the perceptual transforms did improve over using each technique separately, the improvement was minor. However, that work did not explore how well these models performed when applied to display capabilities outside of the training data set. This new work examines what happens when the machinelearning approaches are used to predict quality outside of the training set, both in terms of extrapolation and interpolation. While doing so, we consider two models – one based on physical display characteristics, and a perceptual model that transforms physical parameters based on human visual system models. We found that the use of the perceptual transforms particularly helps with extrapolation, and without their tempering effects, the machine learning-based models can produce wildly unrealistic quality predictions.

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Anustup Choudhury, Scott Daly, "Advantages of Incorporating Perceptual Component Models into a Machine Learning framework for Prediction of Display Qualityin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XV,  2018,  pp 299-1 - 299-6,  https://doi.org/10.2352/ISSN.2470-1173.2018.12.IQSP-299

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Copyright © Society for Imaging Science and Technology 2018
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