Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

No-reference high-dynamic-range image quality assessment based on tensor decomposition and manifold learning

Not Accessible

Your library or personal account may give you access

Abstract

The practical applications of the full-reference image quality assessment (IQA) method are limited. Here, we propose a new no-reference quality assessment method for high-dynamic-range (HDR) images. First, tensor decomposition is used to generate three feature maps of an HDR image, considering color and structure information of the HDR image. Second, for a given HDR image, because its first feature map contains its main energy and important structural feature information, manifold learning is used in the first feature map to find the inherent geometric structure of high-dimensional data in a low-dimensional manifold. In addition, the corresponding multi-scale manifold structure features are extracted from the first feature map. For the second and third feature maps of the HDR image, multi-scale contrast features are extracted, as they reflect the perceived detail contrast information of the HDR image. Finally, the extracted features are aggregated by support vector regression to obtain the objective quality prediction score of the HDR image. Experimental results show that the proposed method is superior to some representative full- and no-reference methods, and even superior to the full-reference HDR IQA method, HDR-VDP-2.2, on the Nantes database. The proposed method has a higher consistency with human visual perception.

© 2018 Optical Society of America

Full Article  |  PDF Article
More Like This
Naturalness index for a tone-mapped high dynamic range image

Yang Song, Gangyi Jiang, Mei Yu, Yun Zhang, Feng Shao, and Zongju Peng
Appl. Opt. 55(35) 10084-10091 (2016)

Full-reference quality assessment of stereoscopic images by learning binocular visual properties

Jian Ma, Ping An, Liquan Shen, and Kai Li
Appl. Opt. 56(29) 8291-8302 (2017)

Supplementary Material (1)

NameDescription
Code 1       source code

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (6)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (9)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (26)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved