.png){kind=icon}
返回
A No-Reference Texture Regularity Metric Based on Visual Saliency
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2015.2416632.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a no-reference perceptual metric that quantifies the degree of perceived regularity in textures. The metric is based on the similarity of visual attention (VA) of the textural primitives and the periodic spatial distribution of foveated fixation regions throughout the image. A ground-truth eye-tracking database for textures is also generated as part of this paper and is used to evaluate the performance of the most popular VA models. Using the saliency map generated by the best VA model, the proposed texture regularity metric is computed. It is shown through subjective testing that the proposed metric has a strong correlation with the mean opinion score for the perceived regularity of textures. The proposed texture regularity metric can be used to improve the quality and performance of many image processing applications like texture synthesis, texture compression, and content-based image retrieval.
Keyword:
Visual attention
texture analysis
randomness
image quality assessment
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:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study视觉显著性建模中人模一致性的定量分析: 一项比较研究
OAAI
OAAI分享收藏分享收藏
查看更多内容