Stochastic perturbation theory
SIAM Review
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Perceptual quality metrics applied to still image compression
Signal Processing - Special issue on image and video quality metrics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An SVD-based grayscale image quality measure for local and global assessment
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
Visual distortion gauge based on discrimination of noticeable contrast changes
IEEE Transactions on Circuits and Systems for Video Technology
Objective quality assessment of MPEG-2 video streams by using CBP neural networks
IEEE Transactions on Neural Networks
A Convolutional Neural Network Approach for Objective Video Quality Assessment
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Objective image quality estimation is useful in many visual processing systems, and is difficult to perform in line with the human perception. The challenge lies in formulating effective features and fusing them into a single number to predict the quality score. In this brief, we propose a new approach to address the problem, with the use of singular vectors out of singular value decomposition (SVD) as features for quantifying major structural information in images and then support vector regression (SVR) for automatic prediction of image quality. The feature selection with singular vectors is novel and general for gauging structural changes in images as a good representative of visual quality variations. The use of SVR exploits the advantages of machine learning with the ability to learn complex data patterns for an effective and generalized mapping of features into a desired score, in contrast with the oft-utilized feature pooling process in the existing image quality estimators; this is to overcome the difficulty of model parameter determination for such a system to emulate the related, complex human visual system (HVS) characteristics. Experiments conducted with three independent databases confirm the effectiveness of the proposed system in predicting image quality with better alignment with the HVS's perception than the relevant existing work. The tests with untrained distortions and databases further demonstrate the robustness of the system and the importance of the feature selection.