With the exponential growth of stereoscopic imaging in various applications, it has become very demanding to have a reliable quality assessment technique to measure the human perception of stereoscopic images. Quality assessment of stereoscopic visual content in the presence of artefacts caused by compression and transmission is a key component of end-to-end 3D media delivery systems. Despite a few recent attempts to develop stereoscopic image/video quality metrics, there is still a lack of a robust stereoscopic image quality metric. Towards addressing this issue, this paper proposes a full reference stereoscopic image quality metric, which mimics the human perception while viewing stereoscopic images. A signal processing model that is consistent with physiological literature is developed in the paper to simulate the behaviour of simple and complex cells of the primary visual cortex in the Human Visual System (HVS). The model is trained with two publicly available stereoscopic image databases to match the perceptual judgement of impaired stereoscopic images. The experimental results demonstrate a significant improvement in prediction performance as compared with several state-of-the-art stereoscopic image quality metrics.
['Gonaduwage Perera', 'D. Varuna S. X. De Silva', 'Ahmet M. Kondoz', 'Safak Dogan']
An improved model of binocular energy calculation for full-reference stereoscopic image quality assessment