基于视觉显著性的局部感知锐度的模糊图像质量评价算法研究

相入喜; 朱锡芳; 吴峰; 许清泉

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基于视觉显著性的局部感知锐度的模糊图像质量评价算法研究

更新时间：2026-09-04

- 基于视觉显著性的局部感知锐度的模糊图像质量评价算法研究
- No-reference Image Blur Assessment Method Based on Local Perceive and Visual Saliency
- 微电子学与计算机 2016年33卷第11期页码：40-44
- 作者机构：
  
  1. 常州工学院机构电气与光电工程学院,江苏,常州,213002
  2. 常州市光电技术研究院,江苏,常州,213002
  3. 常州光电子材料与器件重点实验室,江苏,常州,213002
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61475027常州工学院自然科学基金 YN1208江苏省文化协同创新基金 XYN1408
- DOI：
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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相入喜, 朱锡芳, 吴峰, 许清泉. 基于视觉显著性的局部感知锐度的模糊图像质量评价算法研究[J]. 微电子学与计算机, 2016, 33(11): 40-44.

XIANG Ru-xi, ZHU Xi-fang, WU Feng, et al. No-reference Image Blur Assessment Method Based on Local Perceive and Visual Saliency[J]. 2016, 33(11): 40-44.
相入喜, 朱锡芳, 吴峰, 许清泉. 基于视觉显著性的局部感知锐度的模糊图像质量评价算法研究[J]. 微电子学与计算机, 2016, 33(11): 40-44. DOI：

XIANG Ru-xi, ZHU Xi-fang, WU Feng, et al. No-reference Image Blur Assessment Method Based on Local Perceive and Visual Saliency[J]. 2016, 33(11): 40-44. DOI：

摘要

提出了一种基于视觉显著性和局部感知锐度特征的模糊图像质量评价算法.首先利用有效的视觉显著性检测图像的显著区域

然后结合图像的局部方差和均值

构建有效图像的显著性权值; 其次

分别计算图像的局部感知锐度谱特征和空间局部锐度特征

并然后结合视觉显著性权值形成新的锐度特征图

最后通过分析带权锐度特征图

计算图像质量的最终的评价值.在经典数据库TID2008

CSIQ

和LIVE上验证该算法的有效性

结果表明

与传统的基于局部感知锐度特征的评价算法相比

新算法有效地结合颜色信息和图像显著性

提高模糊图像的评价效果.

Abstract

This paper proposes a new image quality assessment method based on visual saliency and local perceptual sharpness. Firstly

the effective visual saliency image salient region is detected

and then the image local variance and mean construct effective image saliency weights are combined. Secondly

the image sharpness of local spectrum feature of local spatial features and sharpness are respectively computed

and then a new feature sharpness map is formed by combing the visual saliency weight of the image. Finally

the final evaluation index value of image quality is computed by the analysis of the sharpness map.Some experimental results show that the proposed method is effective than traditional evaluation method based on local perceptual sharpness in the classical database TID2008

CSIQ and LIVE because we effectively combine with color information and image saliency and improve the quality evaluation.

关键词

Keywords

references

Goferman Stas, Zelnik-Manor, Lihi, Tal Ayellet. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1915-1926. DOI: 1915

Ninassi A, Le Meur O, Le Callet P, et al. Does where you gaze on an image affect your perception of quality? Applying visual attention to image quality metric[C]// 2007 IEEE International Conference on Image Processing. Texas, San Antonio, 2007(2): 169-172.

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Vu C, Phan T, Chandler D. S3: a spectral and spatial measure of local perceived sharpness in natural images[J]. IEEE Transactions on Image Processing, 2012, 21(3): 934-945. DOI: 934

沈军民, 李俊峰, 戴文战. 结合结构信息和亮度统计的无参考图像质量评价[J], 电子学报, 2016, 44(4): 804-812. DOI: 804

Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE Transactions on Image Processing, 2006, 15(11): 3440-3451. DOI: 3440

Moorthy A K, Bovik A C. Visual importance pooling for image quality assessment[J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(2): 193-201. DOI: 193

Narvekar N, Karam L. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)[J]. IEEE Transactions on Image Processing, 2011, 20(9): 2678-2683. DOI: 2678

苗莹, 易三莉, 贺建峰, 等. 结合梯度信息的特征相似性图像质量评估[J]. 中国图象图形学报, 2015, 20(6): 749-755. DOI: 749

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