The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
Comparative evaluation of visualization and experimental results using image comparison metrics
Proceedings of the conference on Visualization '02
The effects of a visual fidelity criterion of the encoding of images
IEEE Transactions on Information Theory
Image quality assessment based on a degradation model
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Towards cognitive image fusion
Information Fusion
Letter to the Editor: Contrast sensitivity function - A correction
Information Fusion
A new image fusion performance metric based on visual information fidelity
Information Fusion
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Comparative evaluation of fused images is a critical step to evaluate the relative performance of different image fusion algorithms. Human visual inspection is often used to assess the quality of fused images. In this paper, we propose some variants of a new image quality metric based on the human vision system (HVS). The proposed measures evaluate the quality of a fused image by comparing its visual differences with the source images and require no knowledge of the ground truth. First, the images are divided into different local regions. These regional images are then transformed to the frequency domain. Second, the difference between the local regional images in frequency domain is weighted with a human contrast sensitivity function (CSF). The quality of a local regional image is obtained by computing the MSE of the weighted difference images obtained from the fused regional image and source regional images. Finally, the quality of a fused image is the weighted summation of the local regional images quality measures. Our experimental results show that these metrics are consistent with perceptually obtained results.