彩色立体图像质量评价方法

Quality assessment method of color stereoscopic images

现有的大多数立体图像质量评价方法都是将彩色图像转换为灰度图像,从而丧失了色彩信息,不利于对彩色立体图像作出正确评价,针对这一问题,提出了一种彩色立体图像质量评价方法。首先,通过对参考图像对和失真图像对分别进行主成分分析(PCA)融合生成彩色图像,利用彩色小波变换分别提取彩色融合图像的低频系数;然后,把低频系数信息用四元数表示,即将低频系数的色相分量局部均值作为四元数的实部,三基色分量作为四元数的虚部,通过四元数奇异值分解得到奇异值特征向量;最后,对参考图像和失真图像的奇异值特征向量作余弦夹角、巴氏距离、卡方距离,分别作为立体图像质量评价指标。该方法在德克萨斯大学公布的对称失真立体图像库和非对称失真立体图像库分别进行验证,线性相关系数和斯皮尔曼等级相关系数(SROCC)在对称失真库中可高达0.919和0.923,与主观评价吻合度很高。

Most existing stereoscopic image quality assessment methods convert color images to gray scale images, which loses the color information, so it is not conducive for color stereopairs to make the right assessment. To solve this problem, a quality assessment method of color stereopairs was proposed. Firstly, the new algorithm used Principal Component Analysis （PCA） image fusion to deal with the reference image pairs and the distortion image pairs to generate 2D color images, Secondly, the low-frequency coefficients were extracted from the 2D images by color wavelet transform respectively. The information of low-frequency coefficients were expressed in quaternion form. In other words, hue component＇ local mean of low-frequency coefficients was regarded as real part of quaternion, and three primary color components were regarded as the imaginary parts of quaternion. Finally, singular value feature vectors were gained by quaternion singular value decomposition. Cosine angle, Bhattacharyya distance and chi-square distance based on singular value feature vectors were taken as image quality evaluation indexes respectively. The method was tested on the LIVE 3D Image Quality Database, which included both symmetric and asymmetric distorted 3D images published by university of Texas. The linear correlation coefficient and Spearman Rank Order Correlation Coefficient （SROCC） achieved 0. 919 and 0. 923 in symmetric database. The results have high accordance with the subjective evaluation and reach the expected values.