The cortex transform: rapid computation of simulated neural images
Computer Vision, Graphics, and Image Processing
An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models
ACM Transactions on Graphics (TOG)
The use of psychophysical data and models in the analysis of display system performance
Digital images and human vision
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Pairwise classifier combination using belief functions
Pattern Recognition Letters
No-reference JPEG-image quality assessment using GAP-RBF
Signal Processing
Comparing Combination Rules of Pairwise Neural Networks Classifiers
Neural Processing Letters
Objective image quality assessment based on support vector regression
IEEE Transactions on Neural Networks
Image Quality Metrics: PSNR vs. SSIM
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fast pixel classification by SVM using vector quantization, tabu search and hybrid color space
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
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 comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers. The proposed method namely Machine Learning-based Image Quality Measure (MLIQM) first classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method.