Referenceless quality metric of multiply-distorted images based on structural degradation
Referenceless quality metric of multiply-distorted images based on structural degradation
复制标题
基于结构退化的多重畸变图像的无参考质量度量
DOI：
10.1016/j.neucom.2018.02.050
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发表时间：
2018-05-17
期刊：
影响因子：
6
通讯作者：
Xia, Shu-Tao
中科院分区：
文献类型：
--
作者：
Dai, Tao;Gu, Ke;Xia, Shu-Tao
多重失真图像，即被不同类型的失真同时失真的图像，在实际应用中是非常常见的。这类图像包含多个叠加阶段(如采集、压缩和传输阶段)。每个阶段都会引入某种类型的失真，例如采集阶段的传感器噪声和压缩阶段的压缩伪影。然而，目前大多数盲/无参考图像质量评估(NR-IQA)方法都是专门针对单失真图像设计的，因此在处理多失真图像方面存在不足。基于人类视觉系统适应图像结构信息的假设，我们尝试基于结构退化来评估多重失真图像。为此，我们同时使用一阶和高阶图像结构来设计一种新的针对多重失真图像的无参考质量度量。具体地说，我们利用从梯度幅值图和对比度归一化图中提取的质量感知特征，并通过随机子空间方法利用特征的冗余性来进一步提高性能。在常见的多重失真图像库上的实验结果验证了该方法的优异性能。(C)2018爱思唯尔B.V.保留所有权利。
Multiply-distorted images, that is, distorted by different types of distortions simultaneously, are so common in real applications. This kind of images contain multiple overlaying stages (e.g., acquisition, compression and transmission stage). Each stage will introduce a certain type of distortion, for example, sensor noise in acquisition stage and compression artifacts in compression stage. However, most current blind/no-reference image quality assessment (NR-IQA) methods are specifically designed for singly-distorted images, thus resulting in their deficiency in handling multiply-distorted images. Motivated by the hypothesis that human visual system (HVS) is adapted to the structural information in images, we attempt to assess multiply-distorted images based on structural degradation. To this end, we use both first-and high-order image structures to design a novel referenceless quality metric for multiply-distorted images. Specifically, we leverage the quality-aware features extracted from both the gradient-magnitude map and contrast-normalized map, and further improve the performance by making use of redundancy of features with random subspace method. Experimental results on popular multiply-distorted image databases verify the outstanding performance of the proposed method. (c) 2018 Elsevier B.V. All rights reserved.