Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment

Jongyoo Kim, Hui Zeng, Deepti Ghadiyaram, Sanghoon Lee, Lei Zhang, Alan C. Bovik

Research output: Journal article publicationJournal articleAcademic researchpeer-review

138 Citations (Scopus)


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.

Original languageEnglish
Article number8103112
Pages (from-to)130-141
Number of pages12
JournalIEEE Signal Processing Magazine
Issue number6
Publication statusPublished - 1 Nov 2017

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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