HVS-Based Perception-Driven No-Reference Omnidirectional Image Quality Assessment

Abstract

Evaluating the quality of panoramic images has gradually become a hot research topic with the development of virtual reality (VR) technology. Therefore, a novel method is proposed to assess the quality of omnidirectional images without any reference information. Inspired by the characteristics of the human visual system (HVS) and visual attention mechanism, the proposed model is composed of the structure feature, statistical feature, and saliency feature to measure the panoramic image quality, in which structure information is expressed by combining the local Taylor series with the local binary pattern (LBP) operator, gradient-based statistical information of panoramic images are summarized comprehensively from three levels: the gradient measure, the relative gradient magnitude, and the relative gradient orientation, and the saliency detection by combining simple priors (SDSP)-based saliency information is extracted in this article to enrich perception feature of our model and improve the visibility of the saliency region in the omnidirectional image. Finally, according to the subjective scores provided and the above features, we use support vector regression (SVR) to predict the objective scores. The experiments indicate that our model has more substantial competitiveness and stability than other state-of-the-art methods on two reliable databases.