西安电子科技大学学报（自然科学版）2012，Vol.39Issue(4)：155-160,6.DOI:10.3969/j.issn.1001-2400.2012.04.028
视觉相似性图像质量评价方法
Visual similarity index for image quality assessment
摘要
Abstract
Low level features are widely used in computer vision for acquiring information from outside circumstance and responding to it. Considering that low level features provide a rich source of information about luminance distribution, object organization and foreground/background configuration, their difference reflects the structural change of images. Based on the fact that the human vision system always focuses on the local neighborhoods around gazing positions, similarity between corner and edge of images is estimated locally and combined into an image quality metric, namely low-level features based similarity measure (LFSIM). Extensive experiments based upon five publicly-available image databases with subjective ratings demonstrate that LFSIM performs much better than traditional peak signal noise ratio (PSNR) and structural similarity measure (SSIM), and is even competitive to the state-of-the art image quality assessment algorithms information fidelity criteria (IFC) and visual information fidelity (VIF), which are developed on the basis of natural scene statistics.关键词
图像质量/人眼视觉感知/角点/边缘Key words
image quality/ visual perception/corner/edge分类
信息技术与安全科学引用本文复制引用
崔力,陈玉坤,韩宇..视觉相似性图像质量评价方法[J].西安电子科技大学学报（自然科学版）,2012,39(4):155-160,6.基金项目
陕西省自然科学基金资助项目(3011JQ8038) （3011JQ8038）
人事部留学人员科技活动资助项目 （）
西北工业大学基础研究基金资助项目(JC201014) （JC201014）
西北工业大学E之星青年基金资助项目 （）
