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

Fingerprint

Image quality
Neural networks
Learning systems
Image processing
Agglomeration

Keywords

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

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

Cite this

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

A fast no-reference screen content image quality prediction using convolutional neural networks. / Cheng, Zhengxue; Takeuchi, Masaru; Kanai, Kenji; Katto, Jiro.

2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8551572.

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

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., 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018, San Diego, United States, 18/7/23. https://doi.org/10.1109/ICMEW.2018.8551572
Cheng Z, Takeuchi M, Kanai K, Katto J. 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. Institute of Electrical and Electronics Engineers Inc. 2018. 8551572 https://doi.org/10.1109/ICMEW.2018.8551572
Cheng, Zhengxue ; Takeuchi, Masaru ; Kanai, Kenji ; Katto, Jiro. / A fast no-reference screen content image quality prediction using convolutional neural networks. 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018. Institute of Electrical and Electronics Engineers Inc., 2018.
@inproceedings{d30fbb0dc4ce4a7a9e49d14b1a54b494,
title = "A fast no-reference screen content image quality prediction using convolutional neural networks",
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.",
keywords = "Convolutional Neural Networks, No-reference Image Quality Assessment, Screen content images",
author = "Zhengxue Cheng and Masaru Takeuchi and Kenji Kanai and Jiro Katto",
year = "2018",
month = "11",
day = "28",
doi = "10.1109/ICMEW.2018.8551572",
language = "English",
booktitle = "2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A fast no-reference screen content image quality prediction using convolutional neural networks

AU - Cheng, Zhengxue

AU - Takeuchi, Masaru

AU - Kanai, Kenji

AU - Katto, Jiro

PY - 2018/11/28

Y1 - 2018/11/28

N2 - 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.

AB - 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.

KW - Convolutional Neural Networks

KW - No-reference Image Quality Assessment

KW - Screen content images

UR - http://www.scopus.com/inward/record.url?scp=85059986409&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059986409&partnerID=8YFLogxK

U2 - 10.1109/ICMEW.2018.8551572

DO - 10.1109/ICMEW.2018.8551572

M3 - Conference contribution

BT - 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -