publication . Article . Other literature type . 2019

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

Alejandro Moreno; Fabio Carra; Francesco Banterle; Alessandro Artusi;
Open Access English
  • Published: 07 Oct 2019
  • Country: Italy
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...
Persistent Identifiers
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, and HDR imaging, Computer vision, Deep learning, Distortion, Image quality, Human visual system model, Feature extraction, Visualization, Computer science, Digital image processing, Artificial intelligence, business.industry, business, Perception, media_common.quotation_subject, media_common
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
,
EC| ENCORE
Project
ENCORE
ENergy aware BIM Cloud Platform in a COst-effective Building REnovation Context
  • Funder: European Commission (EC)
  • Project Code: 820434
  • Funding stream: H2020 | RIA
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