This paper is an evaluation of four information theoretic image fusion quality assessment metrics and how they perform, in comparison with some of the existing quantitative metrics. The information theoretic fusion metrics evaluated are: Fusion Factor (FF), Fusion Symmetry (FS), Image Fusion Performance Measure (IFPM) and Renyi Entropy (RE). Even though traditional quality assessment metrics like Mean Square Error (MSE), Correlation Coefficient (CC) etc, are being improved by incorporating the edge information, similarity measure between the images, taking the luminance and contrast measures in the images etc, most of the quantitative approaches still don’t give a satisfactory performance, since they don’t take into account the information content in the images. Here, we illustrate how the information theoretic metrics are superior to the quantitative metrics, for grayscale image fusion.
['T. Arathi', 'K. P. Soman']
Performance Evaluation of Information Theoretic Image Fusion Metrics over Quantitative Metrics