.png){kind=icon}
返回
Image quality assessment via multiple features
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-021-11788-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multimedia devices are indispensable in the information society. And, image quality highly impacts user experience of multimedia equipment. Therefore, measuring image quality accurately has great application value. The existing image quality assessment (IQA) methods have demonstrated the natural sense statistics and image structural information can measure the degradation of image. However, the generalization ability of individual IQA method is limited. In this paper, we propose a novel no-reference IQA method which is based on multiple features. For each image, we first extract natural sense statistic feature, global structural feature and local structural feature, respectively. Second, we train the quality prediction model via different features, and obtain different quality prediction scores by the models. Third, the prediction scores are collected and transformed to feature vectors. Subsequently, the IQA model is trained by support vector regression, and the input variables are the obtained feature vectors and subjective scores. The experimental results on the public databases demonstrate the proposed method can accurately predict the quality of both natural image and screen content image, and the performance is competitive with prevalent methods.
Keyword:
No reference
Structural information
Natural sense statistic
Image quality assessment
Support vector regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Tuberculosis diagnosed after chemotherapy for presumed mediastinal malignant neoplasia化疗后诊断为纵隔恶性新生物疑似病例的结核病
PREAI
分享收藏Effect of vacuum arc cathode spot distribution on breaking capacity of the arc-extinguishing chamber真空电弧阴极斑点分布对灭弧室开断能力的影响
PREAI
分享收藏No -reference stereoscopic image quality evaluator with segmented monocular features and perceptual binocular features.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容