Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/219260
Title: Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment
Authors: Jongyoo Kim;Hui Zeng;Deepti Ghadiyaram;Sanghoon Lee;Lei Zhang;Alan C. Bovik
Year: 2017
Publisher: IEEE
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://localhost/handle/Hannan/219260
volume: 34
issue: 6
More Information: 130,
141
Appears in Collections:2017

Files in This Item:
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8103112.pdf2.92 MBAdobe PDF
Title: Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment
Authors: Jongyoo Kim;Hui Zeng;Deepti Ghadiyaram;Sanghoon Lee;Lei Zhang;Alan C. Bovik
Year: 2017
Publisher: IEEE
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://localhost/handle/Hannan/219260
volume: 34
issue: 6
More Information: 130,
141
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8103112.pdf2.92 MBAdobe PDF
Title: Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment
Authors: Jongyoo Kim;Hui Zeng;Deepti Ghadiyaram;Sanghoon Lee;Lei Zhang;Alan C. Bovik
Year: 2017
Publisher: IEEE
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://localhost/handle/Hannan/219260
volume: 34
issue: 6
More Information: 130,
141
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8103112.pdf2.92 MBAdobe PDF