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Open Access Combining Quality Metrics using Machine Learning for improved and robust HDR Image Quality Assessment

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We improve High Dynamic Range (HDR) Image Quality Assessment (IQA) using a full reference approach that combines results from various quality metrics (HDR-CQM). We combine metrics designed for different applications such as HDR, SDR and color difference measures in a single unifying framework using simple linear regression techniques and other non-linear machine learning (ML) based approaches. We find that using a non-linear combination of scores from different quality metrics using support vector machine is better at prediction than the other techniques such as random forest, random trees, multilayer perceptron or a radial basis function network. To improve performance and reduce complexity of the proposed approach, we use the Sequential Floating Selection technique to select a subset of metrics from a list of quality metrics. We evaluate the performance on two publicly available calibrated databases with different types of distortion and demonstrate improved performance using HDR-CQM as compared to several existing IQA metrics. We also show the generality and robustness of our approach using cross-database evaluation.
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Keywords: High Dynamic Range; Machine Learning; Quality Assessment

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Combining quality metrics using machine learning for improved and robust HDR image quality assessment".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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