Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79834
Title: Deep convolutional neural models for picture-quality prediction challenges and solutions to data-driven image quality assessment
Authors: Kim, J
Zeng, H 
Ghadiyaram, D
Lee, S
Zhang, L 
Bovik, AC
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE signal processing magazine, Nov. 2017, v. 34, no. 6, p. 130-141 How to cite?
Journal: IEEE signal processing magazine 
Abstract: Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database.
URI: http://hdl.handle.net/10397/79834
ISSN: 1053-5888
DOI: 10.1109/MSP.2017.2736018
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