基于非局部梯度的图像质量评价算法

高敏娟; 党宏社; 魏立力; 张选德

doi:10.11999/JEIT180597

基于非局部梯度的图像质量评价算法

doi: 10.11999/JEIT180597

1.
陕西科技大学电气与信息工程学院西安 710021
2.
宁夏大学数学统计学院银川 750021

基金项目: 国家自然科学基金(61871260, 61603234, 61362029, 61461043)

详细信息

作者简介:
高敏娟：女，1984年生，博士生，研究方向为图像处理、图像质量评价

党宏社：男，1962年生，教授，博士生导师，研究方向为工业过程与优化、计算机控制、图像处理

魏立力：男，1965年生，教授，研究方向为应用统计与数据分析

张选德：男，1979 年生，教授，博士生导师，研究方向为图像恢复、图像质量评价、稀疏表示和低秩逼近理论

通讯作者:
张选德　zhangxuande@sust.edu.cn

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2018-06-19
- 修回日期: 2018-12-18
- 网络出版日期: 2018-12-26
- 刊出日期: 2019-05-01

Image Quality Assessment Algorithm Based on Non-local Gradient

1.
College of Electrical and Information Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
2.
School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China

Funds: The National Natural Science Foundation of China (61871260, 61603234, 61362029, 61461043)

摘要

摘要:
图像质量评价研究的目标在于模拟人类视觉系统对图像质量的感知过程，构建与主观评价结果尽可能一致的客观评价算法。现有的很多算法都是基于局部结构相似设计的，但人对图像的主观感知是高级的、语义的过程，而语义信息本质上是非局部的，因此图像质量评价应该考虑图像的非局部信息。该文突破了经典的基于局部信息的算法框架，提出一种基于非局部信息的框架，并在此框架内构建了一种基于非局部梯度的图像质量评价算法，该算法通过度量参考图像与失真图像的非局部梯度之间的相似性来预测图像质量。在公开测试数据库TID2008, LIVE, CSIQ上的数值实验结果表明，该算法能获得较好的评价效果。
- 图像质量评价 /
- 人类视觉系统 /
- 非局部梯度
Abstract:
The goal of Image Quality Assessment (IQA) research is to simulate the Human Visual System’s (HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.
- Image Quality Assessment (IQA) /
- Human Visual System (HVS) /
- Non-local gradient

HTML全文

图 1 基于局部和非局部信息的FRIQA模型两步框架

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图 2 参考图像中以$i$为中心、$t$为边长的方邻域

下载: 全尺寸图片幻灯片

图 3 6种算法在TID2008数据库中的散点图

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表 1 10种不同IQA算法在TID2008, CSIQ, LIVE数据库的实验结果比较

数据库	性能指标	PSNR	VSNR	SSIM	MS-SSIM	IW-SSIM	FSIM	ESSIM	GMSD	GSIM	NGSIM
TID2008	SROCC	0.524	0.704	0.774	0.852	0.855	0.880	0.884	0.891	0.855	0.892
	KROCC	0.369	0.534	0.576	0.654	0.663	0.694	0.704	0.708	0.665	0.713
	PLCC	0.530	0.682	0.773	0.842	0.857	0.873	0.885	0.879	0.846	0.886
	RMSE	1.137	0.981	0.851	0.729	0.689	0.652	0.624	0.640	0.715	0.622
CSIQ	SROCC	0.805	0.810	0.875	0.913	0.921	0.924	0.932	0.957	0.912	0.962
	KROCC	0.608	0.624	0.690	0.739	0.752	0.756	0.768	0.813	0.740	0.825
	PLCC	0.800	0.800	0.861	0.899	0.914	0.912	0.922	0.954	0.897	0.961
	RMSE	0.157	0.157	0.133	0.114	0.106	0.100	0.101	0.079	0.115	0.073
LIVE	SROCC	0.875	0.927	0.947	0.944	0.956	0.963	0.962	0.960	0.955	0.950
	KROCC	0.686	0.761	0.796	0.792	0.817	0.833	0.839	0.823	0.813	0.815
	PLCC	0.872	0.923	0.944	0.943	0.952	0.959	0.953	0.960	0.943	0.946
	RMSE	13.36	10.50	8.944	9.095	8.347	7.678	7.003	7.62	9.037	7.455

下载: 导出CSV

表 2 10种不同IQA算法在TID2008，CSIQ, LIVE数据库单一失真性能(SROCC)的比较

数据库	失真类型	PSNR	VSNR	SSIM	MS-SSIM	IW-SSIM	FSIM	ESSIM	GMSD	GSIM	NGSIM
TID2008	AWN	0.907	0.772	0.811	0.809	0.786	0.857	0.885	0.918	0.857	0.902
	ANMC	0.899	0.779	0.803	0.805	0.792	0.851	0.813	0.898	0.809	0.873
	SCN	0.917	0.766	0.815	0.819	0.771	0.848	0.913	0.913	0.890	0.929
	JPEG	0.872	0.917	0.925	0.934	0.918	0.928	0.943	0.952	0.939	0.956
	JP2K	0.813	0.951	0.962	0.973	0.973	0.977	0.975	0.980	0.975	0.958
	J2TE	0.831	0.790	0.858	0.852	0.820	0.854	0.879	0.883	0.892	0.926
CSIQ	AWGN	0.936	0.924	0.897	0.947	0.938	0.926	0.949	0.968	0.944	0.966
	JPEG	0.888	0.903	0.954	0.963	0.966	0.965	0.964	0.965	0.963	0.966
	JP2K	0.936	0.948	0.960	0.968	0.968	0.968	0.967	0.972	0.964	0.974
	FNIOSE	0.933	0.908	0.892	0.933	0.905	0.923	0.943	0.950	0.938	0.962
	BLUR	0.929	0.944	0.960	0.971	0.978	0.972	0.962	0.971	0.958	0.967
	CONTRST	0.862	0.870	0.792	0.952	0.953	0.942	0.939	0.904	0.950	0.946
LIVE	JPEG2	0.895	0.955	0.961	0.962	0.964	0.971	0.980	0.971	0.958	0.972
	JPEG	0.880	0.965	0.976	0.981	0.980	0.983	0.981	0.978	0.909	0.960
	AWGN	0.985	0.978	0.969	0.973	0.966	0.965	0.976	0.974	0.977	0.993
	BLUR	0.782	0.941	0.951	0.954	0.972	0.970	0.991	0.957	0.951	0.939
	FASTFA	0.890	0.902	0.955	0.947	0.944	0.949	0.947	0.942	0.939	0.956

下载: 导出CSV

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