Blind Panoramic Image Quality Assessment via the Asymmetric Mechanism of Human Brain Most existing objective quality evaluation metrics for panoramic images are typically derived from peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they need pristine panoramic images and are not highly consistent with human perception. To address the problem, we propose a novel blind panoramic image quality assessment (PIQA) method to predict the visual quality of panoramic images based on asymmetric mechanism of human brain. In the proposed method, the highfrequency feature is extracted by panoramicweighted local binary pattern and relative total variation is used to clip the highfrequency information in panoramic images, then panoramicweighted statistic feature can represent the lowfrequency feature. Finally, support vector regression (SVR) is adopted to build a quality predictor from feature space to quality score space. The experimental results on the public subjective dataset demonstrate the superiority of our proposed metric compared with stateoftheart objective PIQA methods.

AdaBoost neural network and cyclopean view for noreference stereoscopic image quality assessment Abstract   Stereoscopic imaging has been widely used in many fields. In many scenarios, stereo images quality could be affected by various degradations, such as asymmetric distortion. Accordingly, to guarantee the best quality of experience, robust and accurate referenceless metrics are required for quality assessment of stereoscopic content. Most existing stereo noreference Image Quality Assessment (IQA) models are not consistent with asymmetrical distortions. This paper presents a new noreference stereoscopic image quality assessment metric using a human visual system (HVS) modeling and an advanced machinelearning algorithm. The proposed approach consists of two stages. In the first stage, cyclopean image is constructed considering the presence of binocular rivalry in order to cover the asymmetrically distorted part. In the second stage, gradient magnitude, relative gradient magnitude, and gradient orientation are extracted. These are used as a predictive source of information for the quality. In order to obtain the best overall performance against different databases, Adaptive Boosting (AdaBoost) idea of machine learning combined with artificial neural network model has been adopted. The benchmark LIVE 3D phaseI, phaseII, and IRCCyNIVC 3D databases have been used to evaluate the performance of the proposed approach. Experimental results have demonstrated that the proposed metric performance achieves high consistency with subjective assessment and outperforms the blind stereo IQA over various types of distortion.