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GENERAL PURPOSE APPROACHES FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT

Alaql, Omar abdulrahman
http://rave.ohiolink.edu/etdc/view?acc_num=kent1495821893652963

Abstract Details

2017, PHD, Kent State University, College of Arts and Sciences / Department of Computer Science.
The last decade has witnessed great advances in digital images. Massive numbers of digital images are being captured by mobile digital cameras due to the increasing popularity of mobile imaging devices. These images are subjected to many processing stages during storing, transmitting, or sharing over a network connection. Unfortunately, these processing stages could potentially add visual degradation to original image. These degradations reduce the perceived visual quality which leads to an unsatisfactory experience for human viewers. Therefore, Image Quality Assessment (IQA) has become a topic of high interest and intense research over the last decade. The aim of IQA is to automatically assess image quality in agreement with human judgments. This dissertation mainly focuses on the most challenging category of IQA - general- purpose No-Reference Image Quality Assessment (NR-IQA), where the goal is to assess the quality of images without information about the reference images and without prior knowledge about the types of distortions in the tested image. This dissertation contributes to the research of image quality assessment by proposing three novel approaches for NR- IQA and one model for image distortions classification. First, we propose improvements in image distortions classification by introducing a training model based on new features collection. Second, we propose a NR-IQA technique, which utilizes our improvement in the classification model, and based on a hypothesis that an effective combination of image features can be used to develop efficient NR-IQA approaches. Third, a NR-IQA technique is proposed based on Natural Scene Statistics (NSS) by finding the distance between the natural images and the distorted images in 3D dimensional space. Forth, a novel NR-IQA approach is presented, by utilizing multiple Deep Belief Networks (DBNs) with multiple regression models. We have evaluated the performance of the proposed and some existing models on a fair basis. The obtained results show that our models give better results and yield a significant improvement.
Cheng-Chang Lu (Advisor)
Austin Melton (Committee Member)
Kambiz Ghazinour (Committee Member)
Jun Li (Committee Member)
Mohammed Khan (Committee Member)
116 p.
Computer Science

Recommended Citations

Citations

  • Alaql, O. A. (2017). GENERAL PURPOSE APPROACHES FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1495821893652963

    APA Style (7th edition)

  • Alaql, Omar. GENERAL PURPOSE APPROACHES FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT. 2017. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1495821893652963.

    MLA Style (8th edition)

  • Alaql, Omar. "GENERAL PURPOSE APPROACHES FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT." Doctoral dissertation, Kent State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1495821893652963

    Chicago Manual of Style (17th edition)

kent1495821893652963
166
© 2017, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.