Laboratory for Image and Video Engineering, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
IEEE Trans Image Process. 2011 Dec;20(12):3350-64. doi: 10.1109/TIP.2011.2147325. Epub 2011 Apr 25.
Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: "http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip" xmlns:xlink="http://www.w3.org/1999/xlink">http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.
我们的盲图像质量评估（IQA）方法基于以下假设：自然场景具有一定的统计属性，这些属性在存在失真的情况下会发生变化，从而使其变得不自然；通过使用场景统计来描述这种不自然性，可以识别影响图像的失真并执行无参考（NR）IQA。基于这一理论，我们提出了一种（NR）/盲算法——失真识别图像验证和完整性评估（DIIVINE）指数——它可以在不需要参考图像的情况下评估失真图像的质量。DIIVINE 基于一个涉及失真识别后进行失真特定质量评估的两阶段框架。DIIVINE 能够评估跨多个失真类别的失真图像的质量，而大多数 NR IQA 算法在本质上是特定于失真的。DIIVINE 基于控制自然图像行为的自然场景统计。在本文中，我们详细介绍了 DIIVINE 的基本原则、提取的统计特征及其与感知的相关性，并在流行的 LIVE IQA 数据库上对该算法进行了全面评估。此外，我们将 DIIVINE 的性能与领先的全参考（FR）IQA 算法进行了比较，并证明 DIIVINE 在统计学上优于常用的峰值信噪比（PSNR）度量，并且在统计学上与流行的结构相似性指数（SSIM）相当。DIIVINE 的软件版本已经在网上发布："http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip" xmlns:xlink="http://www.w3.org/1999/xlink">http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip 供公众使用和评估。
