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Image quality assessment (IQA) is useful in many visual processing systems but challenging to perform in line with the human perception. A great deal of recent research effort has been directed towards IQA. In order to overcome the difficulty and infeasibility of subjective tests in many situations, the aim of such effort is to assess visual quality objectively towards better alignment with the perception of the Human Visual system (HVS). In this work, we review and analyze the recent progress in the areas related to IQA, as well as giving our views whenever possible. Following the recent trends, we discuss the engineering approach in more details, explore the related aspects for feature pooling, and present a case study with machine learning. © 2010 SPIE.

World University root node

Department of Electrical Engineering

Proceedings of SPIE - The International Society for Optical Engineering

Perceptual image quality assessment: Recent progress and trends

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. © 2010 IEEE.

IEEE Transactions on Neural Networks

Objective image quality assessment based on support vector regression

We propose a new non-intrusive speech quality assessment algorithm based on Support Vector Regression (SVR) and Mel Frequency Cepstral Coefficients (MFCCs). The basic idea is to map the MFCCs into the desired quality score using SVR. The sensitivity of the MFCCs to external noise is exploited to gauge the changes in the speech signal to evaluate its perceptual quality. 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 perceptual score, in contrast with the oft-utilized feature pooling process in the existing speech quality estimators. Experimental results indicate that the proposed approach outperforms the standard P.563 algorithm for non-intrusive assessment of speech quality with a total of 1792 speech files and the associated subjective scores. © 2010 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Non-intrusive speech quality assessment with support vector regression

Image quality assessment is useful in many visual processing systems and a great deal of research effort has been put in during the recent years to develop objective image quality metrics that correlate well with the perceived quality measurement. Assessing visual quality of images is not easy since the Human Visual System (HVS) is complicated and difficult to be modelled. It is well known that the HVS is sensitive to spatial frequencies and structure in images, so accounting for structure degradation in images is essential for effective picture quality prediction. In this paper, we propose the use of singular vectors out of Singular Value Decomposition as effective structuring elements in images and use them to quantify the loss in structural information in images. The scalability of the proposed metric has been also explored since singular vectors are ordered according to their visual significance. The proposed metric has been validated convincingly on three independent databases (a total of 1196 images of different distortion types and extents), and found to outperform the relevant existing image quality metrics in literature with all circumstances. © 2009 IEEE.

2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09

Scalable image quality assessment based on structural vectors

Arrhythmia is caused due to the changes in the normal rhythm of heart with great risk of fatality if sustained over long periods of time. Many machine-learning algorithms have been developed for a fast diagnosis of cardiac arrhythmia. Also, the decision accuracies vary from method to method with Support Vector Machines (SVM) giving the highest. However, SVM cannot deal with the uncertainties in the process of diagnosis. Therefore, the authors have considered the Rough Set method for this classification. Analysis has been done with and without reducing the number of features. It has been found that the classification accuracies vary between 87% and 54% respectively. It may be emphasized that rough set reasons out all the possibilities and the decision could be as close as human diagnosis. The experimental results are presented. © Springer Science+Business Media B.V. 2008.