IEEE Trans Image Process. 2015 May;24(5):1685-99. doi: 10.1109/TIP.2015.2409558.
In recent years, bandpass statistical models of natural, photographic images of the world have been used with great success to solve highly diverse problems involving image representation, image repair, image quality assessment (IQA), and image compression. One missing element has been a reliable and generic model of spatial image correlation that reflects the distributions of oriented and relatively oriented spatial structures. We have developed such a model for bandpass pristine images and have generalized it here to also capture the spatial correlation structure of bandpass distorted images. The model applies well to both luminance and depth images. As a demonstration of the usefulness of the generalized model, we develop a new no-reference stereoscopic/3D IQA framework, dubbed stereoscopic/3D blind image naturalness quality index, which utilizes both univariate and generalized bivariate natural scene statistics (NSS) models. We first validate the robustness and effectiveness of these novel bivariate and correlation NSS features extracted from distorted stereopairs, then demonstrate that they are predictive of distortion severity. Our experimental results show that the resulting 3D image quality predictor based in part on the new model outperforms state-of-the-art full- and no-reference 3D IQA algorithms on both symmetrically and asymmetrically distorted stereoscopic image pairs.
近年来，带通统计模型已成功应用于自然、摄影世界的图像，用于解决涉及图像表示、图像修复、图像质量评估（IQA）和图像压缩等高度多样化的问题。但仍缺少一个可靠且通用的空间图像相关模型，该模型反映了定向和相对定向空间结构的分布。我们已经为带通原始图像开发了这样的模型，并将其推广到也能捕获带通失真图像的空间相关结构。该模型适用于亮度和深度图像。作为对广义模型有用性的演示，我们开发了一种新的无参考立体/3D IQA 框架，称为立体/3D 盲图像自然度质量指数，该框架利用单变量和广义双变量自然场景统计（NSS）模型。我们首先验证了从失真立体对中提取的这些新的双变量和相关 NSS 特征的稳健性和有效性，然后证明它们可以预测失真严重程度。我们的实验结果表明，部分基于新模型的立体图像质量预测器在对称和非对称失真立体图像对上的性能优于最先进的全参考和无参考 3D IQA 算法。
