Laboratory for Image and Video Engineering, Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA.
IEEE Trans Image Process. 2012 Dec;21(12):4695-708. doi: 10.1109/TIP.2012.2214050. Epub 2012 Aug 17.
We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of "naturalness" in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.
我们提出了一种基于自然场景统计的失真通用盲/无参考（NR）图像质量评估（IQA）模型，该模型在空间域中运行。新模型称为盲/无参考图像空间质量评估器（BRISQUE），它不计算失真特定的特征，例如振铃、模糊或块效应，而是使用局部归一化亮度系数的场景统计信息来量化由于失真而导致图像“自然度”可能的损失，从而实现整体质量度量。所使用的基础特征源自局部归一化亮度的经验分布以及局部归一化亮度乘积在空间自然场景统计模型下的分布。不需要转换到另一个坐标框架（DCT、小波等），这使其与先前的 NR IQA 方法区分开来。尽管简单，我们能够证明 BRISQUE 在统计上优于全参考峰值信噪比和结构相似性指数，并且在所有当前的失真通用 NR IQA 算法中具有很强的竞争力。BRISQUE 具有非常低的计算复杂度，非常适合实时应用。BRISQUE 特征也可用于失真识别。为了说明 BRISQUE 的新实际应用，我们描述了如何使用 BRISQUE 增强非盲图像去噪算法以执行盲图像去噪。结果表明，BRISQUE 增强可以提高现有方法的性能。BRISQUE 的软件版本可在线获得：http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip，供公众使用和评估。
