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Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics

Artusi Alessandro; Banterle Francesco; Carrara Fabio; Moreo Alejandro


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        <foaf:name>Moreo Alejandro</foaf:name>
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    <dct:title>Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics</dct:title>
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    <dcat:keyword>Convolutional Neural Networks (CNNs)</dcat:keyword>
    <dcat:keyword>Objective Metrics</dcat:keyword>
    <dcat:keyword>Image Evaluation</dcat:keyword>
    <dcat:keyword>Human Visual System</dcat:keyword>
    <dcat:keyword>JPEG-XT</dcat:keyword>
    <dcat:keyword>HDR Imaging</dcat:keyword>
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        <foaf:name>European Commission</foaf:name>
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    <dct:description>&lt;p&gt;mage metrics based on Human Visual System&amp;nbsp;(HVS) play a remarkable role in the evaluation of complex image&amp;nbsp;processing&amp;nbsp; algorithms. However, mimicking the HVS is known&amp;nbsp;to be complex and computationally expensive (both in terms&amp;nbsp;of time and memory), and its usage is thus limited to a few&amp;nbsp;applications and to small input data. All of this makes such&amp;nbsp;metrics not fully attractive in real-world scenarios. To address&amp;nbsp;these issues, we propose Deep Image Quality Metric (DIQM), a&amp;nbsp;deep-learning approach to learn the global image quality feature&amp;nbsp;(mean-opinion-score). DIQM can emulate existing visual metrics&amp;nbsp;efficiently, reducing the computational costs by more than an&lt;/p&gt;</dct:description>
    <dct:description xml:lang="">This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</dct:description>
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