Using Image Quality Assessment (IQA) Databases to Provide an Appraisal of the Ability of the Feature Selective Validation Method (FSV) to Compare 2-dimensional Datasets

Date

2017-11-16

Advisors

Journal Title

Journal ISSN

ISSN

0018-9375
1558-187X

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Abstract

This paper investigates the strengths and drawbacks of the recently developed FSV-2D method. Considering that a subjective benchmark for the validation of 2-dimensional computational electromagnetics data is not available, five datasets with subjective scores, commonly used in image quality assessment, are used. It is found that the FSV-2D prediction is influenced by image type and distortion type. Encouraged by the assessment results, eight parameters of the FSV-2D method are optimized by use of genetic algorithms. It is shown that the optimized FSV-2D method provides better correlation with subjective scores. Good agreement with theoretical analysis for computational electromagnetic data further validates the proposed approach.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Feature Selective Validation, Validation, Modeling, Comparisons

Citation

Gang, Z., Orlandi, A. and Duffy, A.P. (2017) Using Image Quality Assessment (IQA) Databases to Provide an Appraisal of the Ability of the Feature Selective Validation Method (FSV) to Compare 2-dimensional Datasets. IEEE Transactions on Electromagnetic Compatibility,60 (4), pp. 890-898

Rights

Research Institute

Institute of Engineering Sciences (IES)