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Keywords:High dynamic range (HDR) video quality Objective quality Spatio-temporal analysis a b s t r a c t High dynamic range (HDR) signals fundamentally differ from the traditional low dynamic range (LDR) ones in that pixels are related (proportional) to the physical luminance in the scene (i.e. scene-referred). For that reason, the existing LDR video quality measurement methods may not be directly used for assessing quality in HDR videos. To address that, we present an objective HDR video quality measure (HDR-VQM) based on signal preprocessing, transformation, and subsequent frequency based decomposition. Video quality is then computed based on a spatio-temporal analysis that relates to human eye fixation behavior during video viewing. Consequently, the proposed method does not involve expensive computations related to explicit motion analysis in the HDR video signal, and is therefore computationally tractable. We also verified its prediction performance on a comprehensive, in-house subjective HDR video database with 90 sequences, and it was found to be better than some of the existing methods in terms of correlation with subjective scores (for both across sequence and per sequence cases). A software implementation of the proposed scheme is also made publicly available for free download and use.

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.

We study the use of machine learning for visual quality evaluation with comprehensive singular value decomposition (SVD)-based visual features. In this paper, the two-stage process and the relevant work in the existing visual quality metrics are first introduced followed by an in-depth analysis of SVD for visual quality assessment. Singular values and vectors form the selected features for visual quality assessment. Machine learning is then used for the feature pooling process and demonstrated to be effective. This is to address the limitations of the existing pooling techniques, like simple summation, averaging, Minkowski summation, etc., which tend to be ad hoc. We advocate machine learning for feature pooling because it is more systematic and data driven. The experiments show that the proposed method outperforms the eight existing relevant schemes. Extensive analysis and cross validation are performed with ten publicly available databases (eight for images with a total of 4042 test images and two for video with a total of 228 videos). We use all publicly accessible software and databases in this study, as well as making our own software public, to facilitate comparison in future research.