A complete no-reference image quality assessment method based on local feature

IF 1.8 Q3 REMOTE SENSING International Journal of Image and Data Fusion Pub Date : 2019-05-05 DOI:10.1080/19479832.2019.1611667

Jiang Wu, Ping Jiang

引用次数: 1

Abstract

ABSTRACT Image quality assessment (IQA) is widely used in digital image processing, and no-reference (NR) IQA has become research focus recently. This paper proposes a NR IQA method based on local features without access to prior knowledge of the images or their distortions. Four gradient masks are used to detect the maximum local gradient (MLG), and the analysis shows that the MLG of strong structure (such as region boundary) includes very tiny noise component, thus this paper assesses image visual quality by using MLGs of strong structures. The proposed method can assess noisy image and blurred image at the same time, and the quality score drops either when the test image becomes blurred or corrupted by random noise. The experiment results show that the proposed approach works well on LIVE, TID2013 and CSIQ databases, and it outperforms some state-of-the-art algorithms.

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一种基于局部特征的完全无参考图像质量评估方法

图像质量评估（IQA）在数字图像处理中得到了广泛的应用，无参考图像质量评估成为近年来的研究热点。本文提出了一种基于局部特征的NR IQA方法，该方法不需要获取图像或其失真的先验知识。使用四个梯度掩模来检测最大局部梯度（MLG），分析表明，强结构（如区域边界）的MLG包含非常微小的噪声分量，因此本文利用强结构的MLG来评估图像视觉质量。该方法可以同时评估噪声图像和模糊图像，当测试图像变得模糊或被随机噪声破坏时，质量分数会下降。实验结果表明，该方法在LIVE、TID2013和CSIQ数据库上运行良好，并且优于一些最先进的算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).