Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment(NR-IQA) method based o...Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment(NR-IQA) method based on the Ada Boost BP neural network in the wavelet domain(WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics(NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the laboratory of image and video engineering(LIVE) database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence of the database and the relatively high operation efficiency of this method.展开更多
图像/视频的获取及传输过程中,由于物理环境及算法性能的限制,其质量难免会出现无法预估的衰减,导致其在实际场景中的应用受到限制,并对人的视觉体验造成显著影响。因此,作为计算机视觉领域的一项重要任务,图像/视频质量评价应运而生。...图像/视频的获取及传输过程中,由于物理环境及算法性能的限制,其质量难免会出现无法预估的衰减,导致其在实际场景中的应用受到限制,并对人的视觉体验造成显著影响。因此,作为计算机视觉领域的一项重要任务,图像/视频质量评价应运而生。其目的在于通过构建计算机数学模型来衡量图像/视频中的失真信息以判断其质量的好坏,达到自动预测质量的效果。在城市生活、交通监控以及多媒体直播等多个场景中具有广泛的应用前景。图像/视频质量评价研究取得了长足的发展,为计算机视觉领域中其他任务提供了一定的便利。本文在广泛调研前人研究的基础上,回顾了整个图像/视频质量评价领域的发展历程,分别列举了传统方法和深度学习方法中一些具有里程碑意义的算法和影响力较大的算法,然后从全参考、半参考和无参考3个方面分别对图像/视频质量评价领域的一些文献进行了综述,具体涉及的方法包含基于结构信息、基于人类视觉系统和基于自然图像统计的方法等;在LIVE(laboratory for image&video engineering)、CSIQ(categorical subjective image quality database)、TID2013等公开数据集的基础上,基于SROCC(Spearman rank order correlation coefficient)、PLCC(Pearson linear correlation coefficient)等评价指标,对一些具有代表性算法的性能进行了分析;最后总结当前质量评价领域仍存在的一些挑战与问题,并对其进行了展望。本文旨在为质量评价领域的研究人员提供一个较全面的参考。展开更多
基金supported by the National Natural Science Foundation of China(61471194 61705104)+1 种基金the Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China(20155552050)the Natural Science Foundation of Jiangsu Province(BK20170804)
文摘Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment(NR-IQA) method based on the Ada Boost BP neural network in the wavelet domain(WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics(NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the laboratory of image and video engineering(LIVE) database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence of the database and the relatively high operation efficiency of this method.
文摘图像/视频的获取及传输过程中,由于物理环境及算法性能的限制,其质量难免会出现无法预估的衰减,导致其在实际场景中的应用受到限制,并对人的视觉体验造成显著影响。因此,作为计算机视觉领域的一项重要任务,图像/视频质量评价应运而生。其目的在于通过构建计算机数学模型来衡量图像/视频中的失真信息以判断其质量的好坏,达到自动预测质量的效果。在城市生活、交通监控以及多媒体直播等多个场景中具有广泛的应用前景。图像/视频质量评价研究取得了长足的发展,为计算机视觉领域中其他任务提供了一定的便利。本文在广泛调研前人研究的基础上,回顾了整个图像/视频质量评价领域的发展历程,分别列举了传统方法和深度学习方法中一些具有里程碑意义的算法和影响力较大的算法,然后从全参考、半参考和无参考3个方面分别对图像/视频质量评价领域的一些文献进行了综述,具体涉及的方法包含基于结构信息、基于人类视觉系统和基于自然图像统计的方法等;在LIVE(laboratory for image&video engineering)、CSIQ(categorical subjective image quality database)、TID2013等公开数据集的基础上,基于SROCC(Spearman rank order correlation coefficient)、PLCC(Pearson linear correlation coefficient)等评价指标,对一些具有代表性算法的性能进行了分析;最后总结当前质量评价领域仍存在的一些挑战与问题,并对其进行了展望。本文旨在为质量评价领域的研究人员提供一个较全面的参考。
