互补色小波域图像质量盲评价方法

图像色彩空间的RGB通道具有密切的关系,图像质量的改变会改变这样的关系.然而传统图像质量评价方法大多基于灰度图像统计特性,忽略了颜色通道间关系信息.为充分利用颜色信息,本文基于新近提出的互补色小波变换提出一种图像质量盲评价方法.文章建立了图像互补色域自然场景统计、多尺度和方向性能量分布等模型.分析表明：这些模型不仅涵盖了传统灰度方法所能描述的信息,而且还能借助于互补色来有效表示彩色图像各通道之间的信息联系,提供表征图像质量的一组高效特征.基于这些特征,我们提出的图像质量盲评价的方法能有效提取图像的失真统计特征,能给出与人眼主观评价图像质量结果保持高度一致、优于现有文献报道盲方法、且可与非盲（全参考）方法相比拟的评价结果.     

基于亮度统计的无参考图像质量评价

基于亮度均值减损对比归一化(MSCN) 系数统计特性及其8方 向邻域系数间的相关性,提出了一种通用无参考图像质量评价方法.首先,分别利用非 对称广义高斯分布(AGGD)模型拟合MSCN系数及其8方 向邻域系数,并估计 相应AGGD 模型参数作为亮度统计特征;其次,计算8方向邻域MSCN系数间的互信息(MI),作为描述方向相 关性的统计特征;进而,分别利用支持向量回归机(SVR)和支 持向量分类机(SVC)构建无参考图像质量评价模型和图像失真类型识别模型; 最后, 在LIVE 等图像质量 评价数据库上进行了算法与DMOS的相关性、失真类型识别、模型 鲁棒性及计算复杂性等方面的实验。实 验结果表明,本文方法的评价结果与人类主观评价具有高度的一致性,在LIVE图像质量评 价数据库上的斯 皮尔曼等级相关系数(SROCC)和皮尔逊线性相关系数 (PLCC)均在0.945以上;而且,图像失真 类型识别模型的识别准确率也高达到92.95%,明显高于 当今主流无参考图像质量评价方法。     

基于奇异值分解与自然图像统计的无参考图像质量评价方法

针对图像质量评价问题,从自然图像统计与SVD角度出发,提出一种通用无参考图像质量评价方法.方法对待测失真图像进行局部归一化,利用奇异值分解提取图像高频信息,采用非对称广义高斯分布进行模拟高频信息的自然图像统计特征,构建图像质量特征向量;利用支持向量机构建图像质量回归模型,实现图像质量评价.通过在LIVE2图像质量评价数...     

基于自然场景统计的无参考图像质量评价算法

为了度量多种失真类型的图像质量,提出一种基于图像空域自然场景统计特征的无参考图像质量评价算法。该算法通过度量失真图像和原始图像在统计规律上的偏差,对失真图像质量做出评价。与现有无参考图像质量评价算法相比,该算法不需要使用原始图像及其失真图像进行训练,也不需要知道图像的失真类型,是一种更具实际意义的通用型无参考图像质量评价算法。同时,考虑到人眼观察图像时感兴趣区域的影响,该算法加入了视觉显著性区域提取的过程。实验结果表明,该算法对于人的主观感知具有较好的一致性。     

面向统计编码的联合比特分配算法

在统计编码系统中,需根据图像复杂度对各路节目进行联合比特分配,比特分配的准确性直接影响了图像质量.因此对图像复杂度的准确评估是统计编码的难点.鉴于传统的基于预测的算法对复杂度评估存在预测误差,提出了将原始帧作为参考帧,通过并行整像素运动估计计算各编码帧的SAD作为统计所需的复杂度信息,提高了图像复杂度评估的准确性,进而提高统计编码性能.通过测试CBR模式下和提出的统计算法下的视频码率以及图像质量来说明所提出算法的可行性.     

基于局部信息失真建模的图像质量评价方法

针对传统的基于像素差值统计的方法以及结构相似度方法不能很好地反映主观评价结果的情况,提出了一种利用图像局部信息失真建模的质量评价方法。该方法通过考虑人眼视觉系统的特点,对像素灰度失真、局部对比度失真和局部结构失真进行建模,并利用局部方差作为权重,得到了最终的图像质量评价测度。其物理意义明确,而且计算相对简单。在LIVE图像数据库上的实验表明,本文方法对于jp2k,jpeg,gblur和fastfading失真的质量预测准确性和一致性都很高,均优于结构相似度方法,对于wn失真也有较好的预测结果。与几种公认较好的方法相比,本文方法表现出了很好的预测性能,得到了与人眼主观感知更加一致的结果。     

全参考图像质量指标评价分析

图像是人类从外界获取信息的重要来源，通过客观的图像质量评价，能帮助人们在浏览图像时关注高质量的图像，提升感观体验。因此，如何对大量的图像质量进行客观而有效的评价，已成为图像信息处理领域的研究热点之一。根据对参考图像（即无失真的原始图像）的依赖程度，客观图像评价方法可分为全参考图像质量评价、部分参考图像质量评价和无参考图像质量评价。针对全参考图像质量评价方法进行分析，比较了常见的4种全参考图像质量评价指标，验证了这些评价指标分数与主观视觉评估相一致。     

一种基于HWD的图像质量评价测度

提出了一种新的图像质量评价测度.首先对图像进行Hybrid Wavelets and Directional Filter Banks (HWD)分解,得到相应的尺度和方向子带信息.然后对各个子带进行对比敏感度掩模,使不同尺度和方向子带的信息具有相同的灵敏度.根据人类视觉感知特性设定视觉感知阈值,获得相应的视觉感知系数并归一化.利用参考图像和失真图像视觉感知系数差值的变化得到相应的图像质量评价测度.实验结果表明本测度与主观感知有很好的一致性,能准确地反映人眼对图像的视觉感知.     

客观评估彩色图像质量的超复数奇异值分解法

本文利用超复数直接对彩色图像建模,保存了彩色图像完整的信息;基于超复数奇异值分解(也称四元数奇异值分解QSVD)提出一种全新的图形化与数值化相结合的彩色图像质量评估测度,不仅能判断图像失真等级,还能判断不同的失真类型.测试结果表明,本文提出的算法比传统的MSE、PSNR以及MSSIM等算法性能更优.     

基于Riesz变换的结构相似度图像质量评价方法

不同的图像处理过程,会对图像引入各种各样的失真,如何对图像的质量进行评价成为一个热点问题。针对传统的基于像素差值统计的峰值信噪比方法及结构相似度方法与人眼主观评价不够符合的情况,本文提出了一种基于Riesz变换的结构相似度图像质量评价方法。该方法先将参考图像和失真图像进行一阶Riesz变换和二阶Riesz变换,并利用得到的5组对应特征图计算出5幅相似度图和5幅权重图,利用平均法进行融合得到最终的相似度图和权重图,然后加入原参考图像和失真图像的亮度比较项,得到最终的图像质量评价指标。在LIVE图像数据库上的实验表明,本文方法对于5种失真的质量预测准确性和一致性都很高,在交叉失真实验中,本文方法也优于结构相似度方法,PLCC和SROCC值达到了0.9482和0.9532。与几种公认较好的方法相比,本文方法能够更好地预测图像质量,更加符合人眼的主观感知。     

Reduced-reference image quality assessment using moment method

Reduced-reference image quality assessment (RR IQA) aims to evaluate the perceptual quality of a distorted image through partial information of the corresponding reference image. In this paper, a novel RR IQA metric is proposed by using the moment method. We claim that the first and second moments of wavelet coefficients of natural images can have approximate and regular change that are disturbed by different types of distortions, and that this disturbance can be relevant to human perceptions of quality. We measure the difference of these statistical parameters between reference and distorted image to predict the visual quality degradation. The introduced IQA metric is suitable for implementation and has relatively low computational complexity. The experimental results on Laboratory for Image and Video Engineering (LIVE) and Tampere Image Database (TID) image databases indicate that the proposed metric has a good predictive performance.     

MLSIM: A Multi-Level Similarity index for image quality assessment

Image quality assessment (IQA) is of great importance to numerous image processing applications, and various methods have been proposed for it. In this paper, a Multi-Level Similarity (MLSIM) index for full reference IQA is proposed. The proposed metric is based on the fact that human visual system (HVS) distinguishes the quality of an image mainly according to the details given by low-level gradient information. In the proposed metric, the Prewitt operator is first utilized to get gradient information of both reference and distorted images, then the gradient information of reference image is segmented into three levels (3LSIM) or two levels (2LSIM), and the gradient information of distorted image is segmented by the corresponding regions of reference image, therefore we get multi-level information of these two images. Riesz transform is utilized to get corresponding features of different levels and the corresponding 1st-order and 2nd-order coefficients are combined together by regional mutual information (RMI) and weighted to obtain a single quality score. Experimental results demonstrate that the proposed metric is highly consistent with human subjective evaluations and achieves good performance.     

Image quality assessment based on Prewitt magnitude

The goal of image quality assessment (IQA) research is to use computational models to calculate the quality of images consistently with subjective evaluations. In this paper, we propose a new image quality assessment (IQA) algorithm by combining Prewitt magnitude and regional mutual information (RMI) in HSV color space. The Prewitt operator is usually used for edge detection and can extract vertical edge more accurately than other operators. The HSV color space encapsulates information about a color in terms that are more natural and intuitive to humans. The proposed method PMRMI first transforms reference and distorted images from RGB color space into HSV color space and Prewitt magnitude is introduced to extract key edge features of each channel. Then the regional mutual information is calculated to measure the similarity of the two images. After that, a weighting method is utilized for better consistency with subjective evaluations. Therefore we get a single quality score. Experiments on various image distortion types demonstrate that the proposed algorithm can achieve better consistency with the subjective evaluations than PSNR and SSIM.     

Reduced reference image quality assessment based on statistics in empirical mode decomposition domain

This paper deals with the image quality assessment (IQA) task using a natural image statistics approach. A reduced reference (RRIQA) measure based on the bidimensional empirical mode decomposition is introduced. First, we decompose both, reference and distorted images, into intrinsic mode functions (IMF) and then we use the generalized Gaussian density (GGD) to model IMF coefficients of the reference image. Finally, we measure the impairment of a distorted image by fitting error between the IMF coefficients histogram of the distorted image and the estimated IMF coefficients distribution of the reference image, using the Kullback–Leibler divergence (KLD). Furthermore, to predict the quality, we propose a new support vector machine-based (SVM) classification approach as an alternative to logistic function-based regression. In order to validate the proposed measure, three benchmark datasets are involved in our experiments. Results demonstrate that the proposed metric compare favorably with alternative solutions for a wide range of degradation encountered in practical situations.     

No-reference image quality assessment based on non-subsample shearlet transform and natural scene statistics

A novel no-reference (NR) image quality assessment (IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform (NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features:coefficient distribution, energy distribution and structural correlation (SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine (SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error (RMSE) with human perception than other high performance NR IQA methods.     

Color image quality assessment based on sparse representation and reconstruction residual

Image quality assessment (IQA) is a fundamental problem in image processing. While in practice almost all images are represented in the color format, most of the current IQA metrics are designed in gray-scale domain. Color influences the perception of image quality, especially in the case where images are subject to color distortions. With this consideration, this paper presents a novel color image quality index based on Sparse Representation and Reconstruction Residual (SRRR). An overcomplete color dictionary is first trained using natural color images. Then both reference and distorted images are represented using the color dictionary, based on which two feature maps are constructed to measure structure and color distortions in a holistic manner. With the consideration that the feature maps are insensitive to image contrast change, the reconstruction residuals are computed and used as a complementary feature. Additionally, luminance similarity is also incorporated to produce the overall quality score for color images. Experiments on public databases demonstrate that the proposed method achieves promising performance in evaluating traditional distortions, and it outperforms the existing metrics when used for quality evaluation of color-distorted images.     

Full-reference IPTV image quality assessment by deeply learning structural cues

Image quality assessment (IQA) attempts to quantify the quality-aware visual attributes perceived by humans. They can be divided into subjective and objective image quality assessment. Subjective IQA algorithms rely on human judgment of image quality, where the human visual perception functions as the dominant factor However, they cannot be widely applied in practice due to the heavy reliance on different individuals. Motivated by the fact that objective IQA largely depends on image structural information, we propose a structural cues-based full-reference IPTV IQA algorithm. More specifically, we first design a grid-based object detection module to extract multiple structural information from both the reference IPTV image (i.e., video frame) and the test one. Afterwards, we propose a structure-preserved deep neural networks to generate the deep representation for each IPTV image. Subsequently, a new distance metric is proposed to measure the similarity between the reference image and the evaluated image. A test IPV image with a small calculated distance is considered as a high quality one. Comprehensive comparative study with the state-of-the-art IQA algorithms have shown that our method is accurate and robust.     

Unsupervised blind image quality assessment based on joint structure and natural scene statistics features

Compared with the widely used supervised blind image quality assessment (BIQA) models, unsupervised BIQA models require little prior knowledge for calculating the objective quality scores of distorted images. In this paper, we propose an unsupervised BIQA method that aims to achieve both good performance and generalization capability with low computational complexity. Carefully selected and extensive structure and natural scene statistics (NSS) features can better represent image quality. First, we employ phase congruency (PC) and finely selected gradient magnitude map and Laplacian of Gaussian response (GM-LOG) features to represent image structure information. Second, we calculate the local mean-subtracted and contrast-normalized (MSCN) coefficients and the Karhunen–Loéve transform (KLT) coefficients to represent the naturalness of the distorted images. Last, multivariate Gaussian (MVG) model with joint features extracted from both the pristine images and the distorted images is adopted to calculate the objective image quality. Extensive experiments conducted on nine IQA databases demonstrate that the proposed method achieves better performance than the state-of-the-art BIQA methods.     

基于非局部梯度的图像质量评价算法

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。