关键词: 双树复小波变换, 非对称广义高斯分布, 梯度幅值, 相对梯度方向, 奇异值, AdaBoosting BP神经网络

Abstract: With the rapid development of 3D technology, stereoscopic images are widely used in many fields. At the same time, it is expected that high-definition images should be provided. So image quality assessment for stereoscopic images has been the concern and a no-reference image quality assessment algorithm for stereoscopic images via Dual-Tree Complex Wavelet Transform（DT-CWT） is proposed. Firstly, the left and the right view images of a stereoscopic image need to be processed in the way of DT-CWT to generate the textural structure image. And the parallax image is obtained according to the principle of minimum energy error. Secondly, features from the textural structure image and the parallax image are extracted, including the parameters of Asymmetric Generalized Gaussian Distribution（AGGD）, variance of Gradient Magnitude（GM） and Relative gradient Orientation（RO）, the area of Singular Value Decomposition（SVD） curve and the coordinate axis. Lastly, it trains AdaBoosting Back-Propagation（BP） neural network by utilizing these features and predicts the quality scores of stereoscopic images. The experiment on LIVE 3D Image Quality Database demonstrates that the predicted scores by the new method have high correlation with mean opinion scores and a good evaluation result is obtained.

Key words: dual-tree complex wavelet transform, asymmetric generalized Gaussian distribution, gradient magnitude, relative gradient orientation, singular value decomposition, AdaBoosting back-propagation neural network