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Open Access Dimension reduction-based attributes selection in no-reference learning-based image quality algorithms

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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 define a new selection process of attributes in no-reference learning-based image quality algorithms. To perform this selection, attributes of seven well known no-reference image quality algorithms are analyzed and compared with respect to degradations present into the image. To assess the performance of these algorithms, the Spearman Rank Ordered Correlation Coefficient (SROCC) is computed between the predicted values and the MOS of three public databases. In addition, an hypothesis test is conducted to evaluate the statistical significance of performance of each tested algorithm.

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Keywords: ATTRIBUTES SELECTION; DIMENSION REDUCTION; IMAGE QUALITY; NO-REFERENCE IMAGE QUALITY ASSESSMENT

Document Type: Research Article

Publication date: 2017-01-29

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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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