No-reference high-dynamic-range image quality assessment based on tensor decomposition and manifold learning

Guan Feifan; Jiang Gangyi; Song Yang; Yu Mei; Peng Zongju; Chen Fen

首页> 外文期刊>Applied optics >No-reference high-dynamic-range image quality assessment based on tensor decomposition and manifold learning

【24h】

No-reference high-dynamic-range image quality assessment based on tensor decomposition and manifold learning

机译：基于张量分解和多流动学习的无参考高动态图像质量评估

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

The practical applications of the full-reference image quality assessment (IQA) method are limited. Here, we propose a new no-reference quality assessment method for high-dynamic-range (HDR) images. First, tensor decomposition is used to generate three feature maps of an HDR image, considering color and structure information of the HDR image. Second, for a given HDR image, because its first feature map contains its main energy and important structural feature information, manifold learning is used in the first feature map to find the inherent geometric structure of high-dimensional data in a low-dimensional manifold. In addition, the corresponding multi-scale manifold structure features are extracted from the first feature map. For the second and third feature maps of the HDR image, multi-scale contrast features are extracted, as they reflect the perceived detail contrast information of the HDR image. Finally, the extracted features are aggregated by support vector regression to obtain the objective quality prediction score of the HDR image. Experimental results show that the proposed method is superior to some representative full-and no-reference methods, and even superior to the full-reference HDR IQA method, HDR-VDP-2.2, on the Nantes database. The proposed method has a higher consistency with human visual perception. (C) 2018 Optical Society of America

机译：全参考图像质量评估（IQA）方法的实际应用有限。在这里，我们提出了一种用于高动态范围（HDR）图像的新的无参考质量评估方法。首先，考虑HDR图像的颜色和结构信息，使用张量分解来生成HDR图像的三个特征映射。其次，对于给定的HDR图像，因为它的第一特征映射包含其主要能量和重要的结构特征信息，所以在第一特征图中使用了歧管学习，以找到低维歧管中的高维数据的固有几何结构。另外，从第一特征图中提取相应的多尺度歧管结构特征。对于HDR图像的第二和第三特征图，提取多尺度对比度特征，因为它们反映了HDR图像的感知细节对比度信息。最后，通过支持向量回归聚合提取的特征以获得HDR图像的目标质量预测得分。实验结果表明，该方法优于一些代表性的全且无参考方法，甚至优于NANTES数据库上的全引用HDR IQA方法HDR-VDP-2.2。该方法具有较高的人类视觉感知的一致性。（c）2018年光学学会

著录项

来源
《Applied optics》 |2018年第4期|共10页
作者
Guan Feifan; Jiang Gangyi; Song Yang; Yu Mei; Peng Zongju; Chen Fen;
展开▼
作者单位

Ningbo Univ Fac Informat Sci &

Engn Ningbo 315211 Zhejiang Peoples R China;

Ningbo Univ Fac Informat Sci &

Engn Ningbo 315211 Zhejiang Peoples R China;

Ningbo Univ Fac Informat Sci &

Engn Ningbo 315211 Zhejiang Peoples R China;

Ningbo Univ Fac Informat Sci &

Engn Ningbo 315211 Zhejiang Peoples R China;

Ningbo Univ Fac Informat Sci &

Engn Ningbo 315211 Zhejiang Peoples R China;

Ningbo Univ Fac Informat Sci &

Engn Ningbo 315211 Zhejiang Peoples R China;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类应用;
关键词

No-reference high-dynamic-range image quality assessment based on tensor decomposition and manifold learning

摘要

著录项

相关主题

期刊订阅