publication . Article . Other literature type . 2019

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

Artusi, Alessandro; Banterle, Francesco; Carra, Fabio; Moreno, Alejandro;
Open Access English
  • Published: 07 Oct 2019
Abstract
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 This work...
Subjects
free text keywords: Convolutional Neural Networks (CNNs), Objective Metrics, Image Evaluation, Human Visual System, JPEG-XT, HDR Imaging, Software, Computer Graphics and Computer-Aided Design, Human visual system model, Deep learning, Feature extraction, Perception, media_common.quotation_subject, media_common, Distortion, Visualization, Computer vision, Image quality, Pattern recognition, Digital image processing, Artificial intelligence, business.industry, business, Mathematics
Funded by
EC| RISE
Project
RISE
Research Center on Interactive Media, Smart System and Emerging Technologies
  • Funder: European Commission (EC)
  • Project Code: 739578
  • Funding stream: H2020 | SGA-CSA
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