Comparison of 3-dimensional datasets by using the generalized n-dimensional (n-D) Feature Selective Validation (FSV) technique

Automatic methods to evaluate the validity of computational electromagnetics computer modeling and simulations have widespread applications. The Feature Selective Validation method is a heuristic technique which has been shown to give broad agreement with visual assessment for 1-dimensional data. As a heuristic technique, extending the dimensionality is an important target for the improvement and development of FSV. One of the major challenges in the development of n-dimensional FSV is the difficulty of obtaining visual assessment results, since the visual comparison of three and higher dimensional data is difficult or even impossible. This paper formulates the comparison of 3-dimensional data based on an established generalized n-dimensional FSV approach. The performance of the approach is investigated by means of the LIVE Video Quality Database which provides subjective scores of 150 distorted videos. A statistical evaluation of the relative performance of FSV and other publicly available full-reference Video Quality Assessment algorithms is presented. Further, parameter tuning is performed to improve the agreement of 3-dimensional FSV results and subjective scores. The proposed approach is finally applied to the self-referenced validation of an electromagnetic simulation model to identify and locate the continuous variation of electric field within a region of space.