J4 ›› 2012, Vol. 39 ›› Issue (4): 155-160.doi: 10.3969/j.issn.1001-2400.2012.04.028

• 研究论文 • 上一篇    下一篇



  1. (1. 西北工业大学 电子信息工程学院,陕西 西安  710072;
    2. 西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西 西安  710071)
  • 收稿日期:2011-12-30 出版日期:2012-08-20 发布日期:2012-10-08
  • 通讯作者: 崔力
  • 作者简介:崔力(1980-),男,讲师,E-mail: l.cui@nwpu.edu.cn.
  • 基金资助:


Visual similarity index for image quality assessment

CUI Li1;CHEN Yukun2;HAN Yu2   

  1. (1. College of Electronic Information, Northwestern Polytechnic Univ., Xi'an  710072, China;
    2. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an  710071, China)
  • Received:2011-12-30 Online:2012-08-20 Published:2012-10-08
  • Contact: CUI Li


低层次视觉特征是计算机视觉系统从环境中获取信息并做出反应的重要依据.考虑到低层次视觉特征包含了图像亮度变化、分布和组织等重要信息,视觉特征差异就反映了图像内在结构的变化.利用人眼视觉感知的局部性与非均匀的特点,分别在角点和边缘特征域测量参考图像和测试图像的相似度,并将其合并为对图像整体质量的度量.在IVC、TID2008、Tomaya-MICT、LIVE和CSIQ图像数据库上,对基于低层次视觉特征的图像质量评价方法和传统的PSNR、SSIM、IFC和VIF方法进行了性能比较.实验表明, 基于低层次视觉特征的图像质量评价方法在整体性能上要远远优于PSNR和SSIM方法,并且能够与公认性能较好的、基于自然场景统计的IFC和VIF方法相媲美.

关键词: 图像质量, 人眼视觉感知, 角点, 边缘


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.

Key words: image quality, visual perception, corner, edge


  • TN911.73