Journal article Open Access

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

Artusi Alessandro; Banterle Francesco; Carrara Fabio; Moreo Alejandro


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>mage metrics based on Human Visual System&nbsp;(HVS) play a remarkable role in the evaluation of complex image&nbsp;processing&nbsp; algorithms. However, mimicking the HVS is known&nbsp;to be complex and computationally expensive (both in terms&nbsp;of time and memory), and its usage is thus limited to a few&nbsp;applications and to small input data. All of this makes such&nbsp;metrics not fully attractive in real-world scenarios. To address&nbsp;these issues, we propose Deep Image Quality Metric (DIQM), a&nbsp;deep-learning approach to learn the global image quality feature&nbsp;(mean-opinion-score). DIQM can emulate existing visual metrics&nbsp;efficiently, reducing the computational costs by more than an</p>", 
  "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "MRG DeepCamera Group, RISE Ltd", 
      "@id": "https://orcid.org/0000-0002-4502-663X", 
      "@type": "Person", 
      "name": "Artusi Alessandro"
    }, 
    {
      "affiliation": "ISTI CNR, Italy", 
      "@type": "Person", 
      "name": "Banterle Francesco"
    }, 
    {
      "affiliation": "ISTI CNR, Italy", 
      "@type": "Person", 
      "name": "Carrara Fabio"
    }, 
    {
      "affiliation": "ISTI CNR, Italy", 
      "@type": "Person", 
      "name": "Moreo Alejandro"
    }
  ], 
  "headline": "Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-10-07", 
  "url": "https://zenodo.org/record/3522907", 
  "version": "Author Manuscript", 
  "keywords": [
    "Convolutional Neural Networks (CNNs)", 
    "Objective Metrics", 
    "Image Evaluation", 
    "Human Visual System", 
    "JPEG-XT", 
    "HDR Imaging"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1109/TIP.2019.2944079", 
  "@id": "https://doi.org/10.1109/TIP.2019.2944079", 
  "@type": "ScholarlyArticle", 
  "name": "Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics"
}
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