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

Zhengxue Cheng, Masaru Takeuchi, Kenji Kanai, Jiro Katto

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538641958
DOI
出版ステータス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
国/地域United States
CitySan Diego
Period18/7/2318/7/27

ASJC Scopus subject areas

  • 信号処理
  • メディア記述

フィンガープリント

「A fast no-reference screen content image quality prediction using convolutional neural networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル