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
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.
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
Research Group:Engineering and Physical Sciences Institute (EPsi)