IEEE Trans Image Process. 2012 Aug;21(8):3378-89. doi: 10.1109/TIP.2012.2197011. Epub 2012 May 1.
Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. Here we propose an RR-IQA method by estimating the structural similarity (SSIM) index, which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We found an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-bydiscretization method is then applied to normalize our measure across image distortion types. We use six publiclyavailable subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application.
基于参考的图像质量评估（RR-IQA）为各种应用中仅能访问部分原始参考图像信息的自动图像质量评估提供了一种实用的解决方案。在此，我们提出了一种 RR-IQA 方法，通过估计结构相似性（SSIM）指数来实现，SSIM 指数是一种广泛使用的全参考（FR）图像质量度量标准，被证明是一种很好的感知图像质量指标。具体来说，我们从多尺度、多方向的除法归一化变换中提取统计特征，并按照 SSIM 构建的理念开发一种失真度量方法。当图像失真类型固定时，我们发现 FR SSIM 度量与我们的 RR 估计之间存在有趣的线性关系。然后，应用一种通过离散化的回归方法对我们的度量进行跨图像失真类型的归一化。我们使用六个公开可用的主观评分数据库来测试所提出的 RR-SSIM 方法，结果表明该方法与 SSIM 和主观质量评估都具有很强的相关性。最后，我们引入了使用 RR 特征部分修复图像的新想法，并以去模糊为例演示了其应用。
