基于视觉特性的图像质量评价

doi:10.19678/j.issn.1000-3428.0064923

摘要/Abstract

摘要：

针对传统图像质量评价算法特征单一、全局和局部特征相关性不高的问题，从人眼视觉特性出发，结合图像全局与局部特征，提出一种基于视觉特性的图像质量评价算法。依据人眼视觉对亮度的响应和位置信息将图像划分为各个视觉区域，根据亮度和位置信息分割小区域，再利用颜色和位置信息对小区域进行合并以生成视觉仿生区域。利用区域内的颜色分布和区域之间的KL距离分别表示局部特征和全局特征，计算图像任意空间近邻区域颜色分布的KL距离以构建二元关系矩阵，即图像区域颜色相似性矩阵。在此基础上，对矩阵进行奇异值分解，提取矩阵的相似性信息，构造图像质量评价指标。在LIVE、CSIQ、TID2008这3个公开图像数据库上进行测试，结果表明，与PSNR、SSIM、BRISQUE、BIQI等算法相比，该算法的Pearson线性相关系数（PLCC）、Spearman等级相关系数（SROCC）、Kendall等级相关系数（KROCC）均取得了稳定且优异的结果，在LIVE和CSIQ数据库上的PLCC、SROCC和KROCC分别达到0.959 0、0.947 8、0.865 3和0.940 4、0.938 8、0.801 7，其评价结果与人眼主观质量评价结果具有高度的一致性，能够较好地评价图像质量。

关键词: 全局特征, 局部特征, 图像质量评价, KL距离, 颜色相似性矩阵

Abstract:

To solve the problems of conventional image quality assessment algorithms, i.e., single features and low correlation between global and local features, visual characteristics based image quality assessment algorithm are proposed based on human visual characteristics and by combining global and local features of images.The image is segmented into several visual regions based on the response of human vision to brightness and location information. Small regions are segmented based on brightness and location information; subsequently, they are merged using color and location information to generate visual bionic regions.Using the color distribution within the region and the Kullback-Leibler(KL) distance between regions to represent local and global features, respectively, the KL distance of the color distribution in adjacent regions in any space of the image is calculated to construct a binary relationship matrix, namely, the image region color similarity matrix.Subsequently, Singular Value Decomposition(SVD) is performed on the matrix to extract the similarity information of the matrix and construct image quality assessment indicators.The results of testing on three public image databases, i.e., LIVE, CSIQ, and TID2008, show that compared with the results of Peak Signal-to-Noise Ratio(PSNR), Structural Similarity(SSIM), Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE), and Blind Image Quality Index(BIQI) algorithms, the Pearson Linear Correlation Coefficient(PLCC), Spearman Rank Order Correlation Coefficient(SROCC), and Kendall Rank Order Correlation Coefficient (KROCC) of the algorithm show stable and excellent results, and the PLCC, SROCC, and KROCC indicate values of (0.959 0, 0.947 8, 0.865 3) on the LIVE database and(0.940 4, 0.938 8, and 0.801 7) on the CSIQ database.The assessment results yielded by the proposed algorithm are highly consistent with the subjective quality assessment results of the human eye; thus the proposed algorithm can accurately evaluate image quality.

Key words: global features, local features, image quality assessment, Kullback-Leibler(KL) distance, color similarity matrix

惠子薇, 何坤, 冯犇, 苏曜. 基于视觉特性的图像质量评价[J]. 计算机工程, 2023, 49(7): 189-195.

Ziwei HUI, Kun HE, Ben FENG, Yao SU. Image Quality Assessment Based on Visual Characteristics[J]. Computer Engineering, 2023, 49(7): 189-195.

https://www.ecice06.com/CN/Y2023/V49/I7/189

图/表 11

图1 仿生视觉区域分割示例

Fig.1 Example of biomimetic visual region segmentation

图2 在CSIQ数据库上的散点图

Fig.2 Scatter diagram on CSIQ database

图3 在LIVE数据库上的散点图

Fig.3 Scatter diagram on LIVE database

图4 在TID2008数据库上的散点图

Fig.4 Scatter diagram on TID2008 database

图5 纹理简单的原始图像及其梯度图

Fig.5 The original image with simple texture and its gradient image

图6 纹理适中的原始图像及其梯度图

Fig.6 The original image with moderate texture and its gradient image

图7 纹理复杂的原始图像及其梯度图

Fig.7 The original image with complex texture and its gradient image

图8 纹理简单图像质量评价结果对比

Fig.8 Comparison of image quality assessment results with simple texture

图9 纹理适中图像质量评价结果对比

Fig.9 Comparison of image quality assessment results with moderate texture

图10 纹理复杂图像质量评价结果对比

Fig.10 Comparison of image quality assessment results with complex texture

表1 不同算法在LIVE、CSIQ、TID2008数据库中的评价指标对比

Table 1 Comparison of assessment indexes of different algorithms in LIVE, CSIQ, and TID2008 databases

算法	LIVE			CSIQ			TID2008
算法	PLCC	SROCC	KROCC	PLCC	SROCC	KROCC	PLCC	SROCC	KROCC
PSNR SSIM NSS CORNIA DLIQA SRNSS BRISQUE NIQE BIQI 本文算法	0.950 8 0.766 3 0.737 6 0.958 4 0.938 2 0.946 7 0.951 9 0.965 2 0.960 6 0.959 0	0.805 1 0.688 1 0.659 8 0.967 9 0.934 5 0.923 3 0.946 3 0.969 0 0.959 4 0.947 8	0.634 6 0.513 9 0.578 4 0.869 0 0.876 0 0.854 7 0.879 9 0.874 3 0.835 1 0.865 3	0.887 0 0.739 6 0.718 6 0.914 7 0.893 9 0.886 7 0.925 0 0.943 5 0.898 4 0.940 4	0.876 3 0.716 7 0.702 2 0.906 5 0.878 6 0.865 0 0.902 6 0.892 5 0.771 3 0.938 8	0.672 1 0.527 0 0.645 3 0.725 3 0.712 8 0.707 8 0.734 4 0.724 2 0.559 1 0.801 7	0.839 2 0.878 2 0.835 6 0.767 3 0.823 5 0.814 9 0.764 8 0.841 0 0.756 8 0.869 2	0.868 2 0.959 6 0.804 7 0.848 1 0.859 0 0.845 4 0.799 6 0.816 4 0.746 8 0.874 8	0.732 8 0.828 8 0.783 1 0.707 1 0.615 8 0.673 9 0.624 1 0.588 9 0.563 4 0.703 7

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