Reduced-reference Quality Assessment Model of Stereoscopic Image Based on Visual Perception and Zero-watermark

Through analyzing the image structure direction characteristics and the stereoscopic perception of human visual system, and combining semi-fragile digital watermarking and Support Vector Regression (SVR), a reduced-reference stereoscopic image quality assessment model based on visual perception and zero-watermark is proposed. In this model, the view zero-watermark is constructed by judging the relation of the horizontal and vertical wavelet coefficients, which can reflect the image structure information. Meanwhile, the disparity zero-watermark that reflects the stereoscopic perception quality is constructed by using the disparity between the left and right views. And then the relativity of two watermark-recovering rates (watermark-recovering rates of the view and disparity zero-watermarks) and subjective quality scores can be learned by the training procedure of SVR. Finally, stereoscopic image quality is predicted by trained SVR. Experimental results show that the proposed reduced-reference model is in accordance with human visual characteristics, and consistent with the result of subjective evaluation value preferably.