Feature detection from local energy
Pattern Recognition Letters
Handbook of Computer Vision and Applications with Cdrom
Handbook of Computer Vision and Applications with Cdrom
Phase congruence measurement for image similarity assessment
Pattern Recognition Letters
No-reference JPEG-image quality assessment using GAP-RBF
Signal Processing
Saliency-Based Image Quality Assessment Criterion
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
A natural image quality evaluation metric
Signal Processing
Content-partitioned structural similarity index for image quality assessment
Image Communication
Structural similarity image quality reliability
Signal Processing
No-reference image quality assessment using structural activity
Signal Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
An SVD-based grayscale image quality measure for local and global assessment
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
Information Content Weighting for Perceptual Image Quality Assessment
IEEE Transactions on Image Processing
SVD Filter Based Multiscale Approach for Image Quality Assessment
ICMEW '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops
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Full reference image quality assessment (FR-IQA) algorithms aim to establish generic measures of perceptual image quality independent of distortion types. Recent developments in FR-IQA have marked the use of phase congruency features. Phase congruency is a dimensionless, normalized feature calculated from the log-Gabor energy of the image, equipped to be relatively insensitive to noise variations due to calculation of noise circle. The underlying assumptions on the nature of the noise used in this calculation affect the performance of phase congruency based FR-IQA measures. In this work, we (a) test the hypothesis that using the phase deviation sensitive energy features obtained from the log-Gabor filtered image instead of the noise adjusted, normalized phase congruency features will improve the general applicability of an FR-IQA measure, (b) reduce execution time by omitting noise circle calculation and (c) study how the modifications in parameter values, changes the correlation between the subjective scores and objective image quality values. Experiments on six benchmark databases suggest the effectiveness of the proposed method which improves over the existing phase congruency based algorithms, achieves competitive performance with the state-of-the-art methods and delivers the best average performance across all databases in terms of prediction monotonicity and accuracy.