A fast no-reference screen content image quality prediction using convolutional neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image quality assessment (IQA) is an inherent research topic in image processing field for several decades. Recently, machine learning has achieved success in many multimedia tasks and can be applied in IQA. Especially, screen content images (SCIs) is greatly increasing in various applications, but the characteristics of SCIs makes it difficult to directly apply general IQA methods to predict qualities. In this paper, we propose a fast no-reference SCIs quality prediction method. First, we use the convolutional neural networks (CNNs) to predict the quality scores of each patch. Second, we present a SCIs-oriented quality aggregation algorithm for acceleration. Experimental results demonstrate that our method can achieve the high accuracy (0.957) with subjective quality scores, outperforming existing methods. Moreover, our method is computationally appealing, achieving flexible complexity performance by selecting different groups of patches.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538641958
DOIs
Publication statusPublished - 2018 Nov 28
Event2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018 - San Diego, United States
Duration: 2018 Jul 232018 Jul 27

Other

Other2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
CountryUnited States
CitySan Diego
Period18/7/2318/7/27

Keywords

  • Convolutional Neural Networks
  • No-reference Image Quality Assessment
  • Screen content images

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

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    Cheng, Z., Takeuchi, M., Kanai, K., & Katto, J. (2018). A fast no-reference screen content image quality prediction using convolutional neural networks. In 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018 [8551572] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMEW.2018.8551572