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: Contribution to journalArticle

87 Citations (Scopus)

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.

Original languageEnglish
Article number8103112
Pages (from-to)130-141
Number of pages12
JournalIEEE Signal Processing Magazine
Volume34
Issue number6
DOIs
Publication statusPublished - 2017 Nov

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment'. Together they form a unique fingerprint.

  • Cite this