No-reference JPEG-image quality assessment using GAP-RBF
Signal Processing
Image Quality Assessment Based on Structural Distortion and Image Definition
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
A natural image quality evaluation metric
Signal Processing
Range image quality assessment by Structural Similarity
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Compress-image quality measures in image-processing applications
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Wireless Video Quality Assessment: A Study of Subjective Scores and Objective Algorithms
IEEE Transactions on Circuits and Systems for Video Technology
An information-theoretic framework for image complexity
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
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The need to obtain objective values of the quality of distorted images with respect to the original is fundamental in multimedia and image processing applications. It is generally required that this value correlates well with the human vision system (HVS). In spite of the properties and the general use of the mean square error (MSE) measurement, this has a poor correlation with HSV, which has led to the development of methods such as structural similarity (SSIM). This metric improves the correlation with respect to the classic MSE and PSNR (peak signal to noise ratio). However, its behavior depends on the values assigned to constants and on the windows size selected. These values are usually assigned arbitrarily and there have been no studies on how they affect the SSIM. In this work, we have analyzed empirically the most appropriate values for the different constants used in the SSIM equations. We have also analyzed the importance of window size in the calculation of MSSIM, and propose a method for determining the window size based on image complexity. Using the values selected and the window size defined, the correlation between SSIM and DMOS (differential mean opinion score) is significantly improved by around 17% with respect to the values commonly used.