Journal article Embargoed 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="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <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: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 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.</dc:description>
  <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>HDR Imaging</dc:subject>
  <dc:title>Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics</dc:title>
Views 30
Downloads 11
Data volume 238.0 MB
Unique views 28
Unique downloads 10


Cite as