Journal article Open Access

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

Artusi Alessandro; Banterle Francesco; Carrara Fabio; Moreo Alejandro


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Artusi Alessandro</dc:creator>
  <dc:creator>Banterle Francesco</dc:creator>
  <dc:creator>Carrara Fabio</dc:creator>
  <dc:creator>Moreo Alejandro</dc:creator>
  <dc:date>2019-10-07</dc:date>
  <dc:description>mage metrics based on Human Visual System (HVS) play a remarkable role in the evaluation of complex image processing  algorithms. However, mimicking the HVS is known to be complex and computationally expensive (both in terms of time and memory), and its usage is thus limited to a few applications and to small input data. All of this makes such metrics not fully attractive in real-world scenarios. To address these issues, we propose Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature (mean-opinion-score). DIQM can emulate existing visual metrics efficiently, reducing the computational costs by more than an</dc:description>
  <dc:description>This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</dc:description>
  <dc:identifier>https://zenodo.org/record/3522907</dc:identifier>
  <dc:identifier>10.1109/TIP.2019.2944079</dc:identifier>
  <dc:identifier>oai:zenodo.org:3522907</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/739578/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/rise-teaming-cyprus</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</dc:rights>
  <dc:source>IEEE Transactions on Image Processing 1-14</dc:source>
  <dc:subject>Convolutional Neural Networks (CNNs)</dc:subject>
  <dc:subject>Objective Metrics</dc:subject>
  <dc:subject>Image Evaluation</dc:subject>
  <dc:subject>Human Visual System</dc:subject>
  <dc:subject>JPEG-XT</dc:subject>
  <dc:subject>HDR Imaging</dc:subject>
  <dc:title>Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
59
18
views
downloads
Views 59
Downloads 18
Data volume 389.5 MB
Unique views 53
Unique downloads 17

Share

Cite as