Quality measurement for monochrome compressed images in the past 25 years
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
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
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
A framework for photo-quality assessment and enhancement based on visual aesthetics
Proceedings of the international conference on Multimedia
Semantic analysis and retrieval in personal and social photo collections
Multimedia Tools and Applications
A holistic approach to aesthetic enhancement of photographs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
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Automatic image quality assessment has many diverse applications. Existing quality measures are not accurate representatives of the human perception. We present a hybrid image quality (HIQ) measure, which is a combination of four existing measures using an `n' degree polynomial to accurately model the human image perception. First we undertook time consuming human experiments to subjectively evaluate a given set of training images, and resultantly formed a Human Perception Curve (HPC). Next we define a HIQ measure that closely follows the HPC using curve fitting techniques. The HIQ measure is then validated on a separate set of images by similar human subjective experiments and is compared to the HPC.The coefficients and degree of the polynomial are estimated using regression on training data obtained from human subjects. Validation of the resultant HIQ was performed on a separate validation data. Our results show that HIQ gives an RMS error of 5.1 compared to the best RMS error of 5.8 by a second degree polynomial of an individual measure HVS (Human Visual System) absolute norm (H 1 ) amongst the four considered metrics. Our data contains subjective quality assessment (by 100 individuals) of 174 images with various degrees of fast fading distortion. Each image was evaluated by 50 different human subjects using double stimulus quality scale, resulting in an overall 8,700 judgements.