[1]童 冰.基于支持向量回归的无参考屏幕内容图像质量评估[J].南京师范大学学报(工程技术版),2021,(01):057-63.[doi:10.3969/j.issn.1672-1292.2021.01.009]
 Tong Bing.No Reference Quality Assessment for ScreenContent Image Based on SVR[J].Journal of Nanjing Normal University(Engineering and Technology),2021,(01):057-63.[doi:10.3969/j.issn.1672-1292.2021.01.009]
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基于支持向量回归的无参考屏幕内容图像质量评估
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年01期
页码:
057-63
栏目:
计算机科学与技术
出版日期:
2021-03-15

文章信息/Info

Title:
No Reference Quality Assessment for ScreenContent Image Based on SVR
文章编号:
1672-1292(2021)01-0057-07
作者:
童 冰
漳州职业技术学院信息工程学院,福建 漳州 363000
Author(s):
Tong Bing
School of Information Engineering,Zhangzhou Institute of Technology,Zhangzhou 363000,China
关键词:
屏幕内容图像无参考质量评估支持向量回归边缘特征亮度特征
Keywords:
screen content imageno reference quality assessmentsupport vector regressionedge featureluminance feature
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2021.01.009
文献标志码:
A
摘要:
提出一种新的基于支持向量回归的无参考屏幕内容图像质量评估算法. 首先,利用高斯差分函数计算边缘图,通过边缘图提取边缘特征; 其次,通过局部归一化获得亮度图,根据亮度图统计亮度特征; 最后,利用支持向量回归算法将质量感知特征映射为主观分数. 在两个数据集上的实验结果表明,所提算法的性能优于大部分现有算法.
Abstract:
The paper presents a new no reference quality assessment algorithm for screen content image based on support vector regression. Firstly,we calculate the edge map through the difference of Gaussians,which is further used to extract edge features. Secondly,we obtain the luminance map through the local normalization,which is further used to compute luminance features. Finally,we map the quality-aware features to subjective scores through the support vector regression algorithm. Experimental results on two data sets show that the performance of the proposed algorithm is better than those of the most of the existing algorithms.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-08-08.
基金项目:福建省中青年教师教育科研基金项目(JA15687).
通讯作者:童冰,讲师,研究方向:计算机视觉与图像处理. E-mail:miran963@163.com
更新日期/Last Update: 2021-03-15