Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
复制标题
梯度幅度相似度偏差：一种高效的感知图像质量指数
DOI：
10.1109/tip.2013.2293423
复制
发表时间：
2014-02-01
影响因子：
10.6
通讯作者：
Bovik, Alan C.
中科院分区：
文献类型：
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作者：
Xue, Wufeng;Zhang, Lei;Bovik, Alan C.
在图像压缩、图像恢复和多媒体流等许多应用中，如实地评估输出图像的感知质量是一项重要的任务。良好的图像质量评估（IQA）模型不仅应该提供高质量的预测精度，而且还应该具有计算效率。由于高速网络中大量视觉数据的不断增加，IQA 指标的效率变得尤为重要。我们提出了一种新的有效且高效的 IQA 模型，称为梯度幅度相似度偏差（GMSD）。图像梯度对图像畸变敏感，而畸变图像中的不同局部结构会遭受不同程度的退化。这促使我们探索使用基于梯度的局部质量图的全局变化来进行整体图像质量预测。我们发现，参考图像和失真图像之间的像素级梯度幅度相似度（GMS）与新颖的池化策略（GMS 图的标准差）相结合，可以准确预测感知图像质量。由此产生的 GMSD 算法比大多数最先进的 IQA 方法快得多，并提供极具竞争力的预测精度。 GMSD的MATLAB源代码可以在http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm下载。
It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.