| 引用本文: | 朱新山,姚思如,孙彪,钱永军.图像质量评价:融合视觉特性与结构相似性指标[J].哈尔滨工业大学学报,2018,50(5):121.DOI:10.11918/j.issn.0367-6234.201709138 |
| ZHU Xinshan,YAO Siru,SUN Biao,QIAN Yongjun.Image quality assessment: Combining the characteristics of HVS and structural similarity index[J].Journal of Harbin Institute of Technology,2018,50(5):121.DOI:10.11918/j.issn.0367-6234.201709138 |
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| 摘要: |
| 图像质量评价是多媒体领域的一个基本问题.在现有结构相似性方案的基础上，增加最小可辨失真和视觉显著性评价用于构造一个新的图像质量评价指标.首先，考虑到人眼视觉系统的掩蔽效应，设计了失真图像的修正模型，以Sigmoid函数为基础，利用最小可辨失真阈值和图像的像素变化绝对值构建失真图像修正模型，对失真图像进行修正，使其符合人眼的感知效果；然后，考虑到人眼视觉系统的视觉注意特性，设计了图像区域权重模型，利用视觉显著性图像表征图像感兴趣内容，作为图像不同区域的权重；最后，计算修正后的失真图像与原图像的局部结构相似性，并利用区域权重对局部区域质量进行加权平均获得全局图像的质量评价值.实验结果表明，与同类度量指标相比，本文提出的指标在评价图像局部质量方面，更符合人的主观感知效果；在主观数据拟合方面，其均方根误差、相关系数等指标都得到提升；在运行效率方面，具有一个适中的计算复杂度，远低于性能优越的度量指标的运行时间.
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| 关键词: 图像质量评价 人眼视觉系统 结构相似性指标 最小可辨失真 显著性图像 |
| DOI：10.11918/j.issn.0367-6234.201709138 |
| 分类号:TN911.73 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61401303；51578189)；国家留学基金(201506255067)；信息安全国家重点实验室开放课题(2017-MS-11)；天津大学自主创新基金(1705) |
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| Image quality assessment: Combining the characteristics of HVS and structural similarity index |
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ZHU Xinshan1,2,YAO Siru1,SUN Biao1,QIAN Yongjun1
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(1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; 2. State Key Laboratory of Information Security (Institute of Information Engineering, Chinese Academy of Sciences), Beijing 100093, China)
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| Abstract: |
| Image quality assessment is a basic problem in the multimedia field. A new image quality metric is constructed based on Structural Similarity (SSIM) index, by exploiting Human Visual System (HVS) characteristics. First, considering the masking effects of HVS, the distorted image is preprocessed by a distortion model with the input of the error between it and the original one and just noticeable distortions (JNDs) derived from a human visual model, where Sigmoid function is explored. The process makes the visible errors in the modified distorted image to be more notable. Second, considering the visual attention characteristics of HVS, the image area weight model is designed to quantify the importance of image local regions for the visual quality. The interesting content of an image can be represented by the saliency image, from which the weights of different regions are obtained. Finally, the local SSIM between the modified distorted image and original image is calculated, and the global image quality metric can be expressed by weighting all local quality with the normalized regional weights. Compared with the state-of-the-art image metrics, the proposed metric fits subjective visual quality better in evaluating the local image quality, has better performance in terms of the mean square error, correlation coefficient and other indicators for predicting the subjective image quality, and has a moderate computational complexity, well below the run time of the superior performance metrics.
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| Key words: image quality assessment human visual system structural similarity index just noticable distortion saliency image |
