计算机科学 ›› 2014, Vol. 41 ›› Issue (11): 309-312.doi: 10.11896/j.issn.1002-137X.2014.11.061

• 图形图像与模式识别 • 上一篇    下一篇

自然图像颜色空间统计规律性研究

褚江,陈强   

  1. 南京理工大学计算机科学与工程学院 南京210094;南京理工大学计算机科学与工程学院 南京210094
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受江苏省青蓝工程和中央高校基本科研业务费专项资金资助

Research on Natural Scene Statistics in Color Space

CHU Jiang and CHEN Qiang   

  • Online:2018-11-14 Published:2018-11-14

摘要: 自然图像统计模型(NSS)在无参考图像质量评价中得到了广泛应用,但目前绝大部分的图像质量评价方法都是针对灰度图像的,没有有效地利用彩色空间的信息。对RGB、HSV、LAB、YCBCR、YIQ 5种颜色空间的规律性进行分析,对归一化的系数值使用高斯分布、对数正态分布、极值分布和T分布进行拟合,对拟合结果进行分析和比较,从中找出最适合各个色彩空间的模型。然后使用拟合成高斯模型的参数作为特征对LIVE库的失真图像进行分类。实验结果表明,某些色彩分量分类效果要优于灰度空间。

关键词: 图像质量评价,自然图像统计,色彩空间,图像处理

Abstract: Natural scene statistics has been widely used in blind image quality assessment,but most assessment methods are designed for gray images,and color space information is not properly used.We studied 5 color spaces (RGB,HSV,LAB,YCBCR,YIQ),and then used Gaussian distribution,logistic distribution,extreme distribution and T distribution to model the normalized coefficients,in order to find the best model for the color space.Then we used Gaussian model parameters as feature to classify the distorted images in LIVE database.We found out that the classify precision in some color space outperforms that in gray-scale statistics.

Key words: Image quality assessment,Natural scene statistics,Color space,Image processing

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