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

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトル2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538641958
DOI
出版物ステータスPublished - 2018 11 28
イベント2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018 - San Diego, United States
継続期間: 2018 7 232018 7 27

Other

Other2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
United States
San Diego
期間18/7/2318/7/27

    フィンガープリント

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

これを引用

Cheng, Z., Takeuchi, M., Kanai, K., & Katto, J. (2018). A fast no-reference screen content image quality prediction using convolutional neural networks. : 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