Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151728
Title: Statistical and structural information backed full-reference quality measure of compressed sonar images
Authors: Chen, Weiling
Gu, Ke
Lin, Weisi
Yuan, Fei
Cheng, En
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Chen, W., Gu, K., Lin, W., Yuan, F. & Cheng, E. (2020). Statistical and structural information backed full-reference quality measure of compressed sonar images. IEEE Transactions On Circuits and Systems for Video Technology, 30(2), 334-348. https://dx.doi.org/10.1109/TCSVT.2019.2890878
Journal: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: In sonar applications, important information such as distributions of minerals, underwater creatures has a high probability of being contained in sonar images. In many underwater applications such as underwater rescue and biometric tracking, it is necessary to send sonar images underwater for further analysis. Due to the bad conditions of underwater acoustic channel and current underwater acoustic communication technologies, sonar images very possibly suffer from several typical types of distortions. As far as we know, limited efforts have been made to gather meaningful sonar image databases and benchmark reliable objective quality model, so far. This paper develops a new objective sonar image quality predictor (SIQP), whose core is the combination of two features specific to a quality measure of sonar images. These two features, which come from statistical and structural information inspired by the characteristics of sonar images and the human visual system, reflect image quality from the global and detailed aspects. The performance comparison of the proposed metric with popular and prevailing quality evaluation models is conducted using a newly established sonar image quality database. The results of experiments show the superiority of our SIQP metric over the available quality evaluation models.
URI: https://hdl.handle.net/10356/151728
ISSN: 1051-8215
DOI: 10.1109/TCSVT.2019.2890878
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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