关键词: 立体图像质量评价, 主成分分析融合, 彩色小波变换, 四元数

Abstract:

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.

Key words: quality assessment of stereoscopic image, Principal Component Analysis (PCA) fusion, color wavelet transform, quaternion