基于特征解纠缠表示的无参考图像质量评价

无参考图像质量评价是在不给定参考图像的情况下评估受损图像的质量。由于缺少无损的参考图像,无参考图像质量评价方法的表现与全参考图像质量评价方法相比差距较大。为了解决这一问题,提出一种基于特征解纠缠表示的无参考图像质量评价方法。该方法能分离出受损图像的内容特征和受损信息特征,通过内容特征对受损图像进行复原,使用孪生卷积网络从受损图像和复原图像中提取图像特征,并将其与受损信息特征进行融合,进而预测受损图像的质量。在TID 2013数据集上,提出的无参考图像评价方法的Spearman秩相关系数与Pearson线性相关系数分别为0.885和0.876,性能优良。     

基于多特征融合和卷积神经网络的无参考图像质量评价

为了更好地评价图像质量,解决在基于卷积神经网络的图像质量评价模型(CNN-IQA)上明显忽略的分块图像之间存在差异性的问题,提出了一种多特征融合的CNN模型.首先,将整幅图像进行不重叠分块,并提取每个分块图像的信息熵和纹理特征.然后,将提取计算的两特征相结合,计算各分块图像的重要性权重,以此衡量分块图像对失真图像质量的...     

基于自然统计特征分布的无参考图像质量评价

针对目前的无参考评价方法无法准确反映人类对图像质量的视觉感知效果,该文提出一种基于自然统计特征分布(DIstribution Characteristics of Natural, DICN)的无参考图像质量评价方法。其原理是用小波变换将图像分解为低频子带和高频子带部分,再将高频子带部分分成 的小块,提取每一子块的幅值和信息熵,并分别计算其分布直方图均值和斜度作为特征,利用支持向量回归思想对特征进行训练,建立5种不同失真类型的质量预测模型。在此基础上,采用支持向量机针对图像特征构造分类器并进行失真判断以确定不同失真的权重,结合5种失真评价模型可得到自然统计特征分布的无参考评价模型。实验结果分析表明,该算法的评价效果优于现有的经典算法,与主观评价具有较好一致性,能够准确反映人类对图像质量的视觉感知效果。     

基于特征符号化和Transformer的无参考图像质量评价方法

基于深度学习的无参考图像质量评价方法目前存在语义关联性不足或模型训练要求高的问题,为此,本文提出了一种基于语义特征符号化和Transformer的无参考图像质量评价方法。首先使用深层卷积神经网络提取图像的高层语义特征;然后将语义特征映射成视觉特征符号,并基于Transformer自注意力机制对视觉特征符号之间的关系进行建模,提取图像的全局特征,同时使用浅层神经网络提取底层局部图像特征,捕捉图像低级失真信息;最后结合全局图像信息与局部图像信息,对图像质量进行预测。为了验证模型的精度和鲁棒性,以相关系数PLCC和SROCC作为评价指标,在5个主流的图像质量评价数据集和1个水下图像质量评价数据集上进行了实验,并将本文提出的方法与15种传统和基于深度学习的无参考图像质量评价方法进行了对比。实验结果表明,本文方法以较少的参数量（大约1.56 MB）在各类数据集上均取得了优越的性能,尤其在多重失真数据集LIVE-MD上将SROCC提升到了0.958,证明在复杂的失真情况下仍能准确评估图像质量,本文网络结构能满足实际应用场景。     

基于像素失真耦合边缘特征融合的无参考质量评价

为了适应多种类型的模糊图像进行质量评价,提高评价模型对图像模糊和振铃的洞察能力,提出了一种像素失真与边缘特征融合的无参考质量评价算法.首先,根据像素失真理论,计算图像像素的标准差和绝对差分值,提取图像的像素特征;然后,计算图像水平和垂直方向的过零率,并利用边缘保持滤波器对图像边缘信息进行测量,精确提取图像的边缘特征;最后,利用提取的像素特征和边缘特征,定义特征融合函数,并引入粒子群优化(PSO)对融合函数参数进行优化,提高对图像模糊和振铃的洞察能力,根据融合特征构建图像质量评价模型.与当前无参考质量评价算法比较,所提算法能够有效地对JPEG(Joint Photographic Experts Group)、JPEG2000(Joint Photographic Experts Group 2000)、模糊等失真图像进行质量评价,评价指标CC(Correlation Coefficient)与SROCC(Spearman Rank-order Correlation Coefficient)达0.9477和0.9153.该算法与主观评价方法具有较好的一致性,能够较好地适用于多种类型的失真图像评价.     

无参考图像质量评价

图像质量评价是近几年图像处理领域比较热门的研究课题。目前,许多学者已经提出了各种各样的无参考质量评价方法。对无参考方法进行综述,详细介绍BIQI,BLIINDS-Ⅱ,BRISQUE,DESIQUE,DIIVINE,NIQE,SSEQ等无参考质量评价方法,并在LIVE和TID2008数据库上进行实验分析,最后根据分析的结果探讨图像质量评价的发展方向。     

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...     

采用边缘信息的屏幕图像质量评价

考虑到人类视觉系统（HVS）对边缘信息敏感且屏幕图像中包含大量边缘信息,本文提出采用边缘信息的屏幕图像质量评价方法。该方法首先从空域和频域分别提取参考和失真屏幕图像的边缘信息进而得到边缘信息相似度图,接着基于边缘信息提取屏幕图像中人眼感兴趣区域,最后利用感兴趣区域加权对所得边缘相似度图进行融合计算以获取最终评价分数值。实验结果表明所提算法具有较高的图像质量评价主客观一致性,其性能优于多个最新图像质量评价方法。      

An optimized CNN-based quality assessment model for screen content image

Most existing convolutional neural network (CNN) based models designed for natural image quality assessment (IQA) employ image patches as training samples for data augmentation, and obtain final quality score by averaging all predicted scores of image patches. This brings two problems when applying these methods for screen content image (SCI) quality assessment. Firstly, SCI contains more complex content compared to natural image. As a result, qualities of SCI patches are different, and the subjective differential mean opinion score (DMOS) is not appropriate as qualities of all image patches. Secondly, the average score of image patches does not represent the quality of entire SCI since the human visual system (HVS) is sensitive to image patches containing texture and edge information. In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference (FR) and no-reference (NR) SCI quality assessment to overcome these two problems. The contribution of our algorithm can be concluded as follows: 1) Considering the characteristics of SCIs, a valid network architecture is designed for both NR and FR visual quality evaluation of SCIs, which makes the networks learn the feature differences for FR-IQA; 2) with the consideration of correlation between local quality and DMOS, a training data selection method is proposed to fine-tune the pre-trained model with valid SCI patches; 3) an adaptive pooling approach is employed to fuse patch quality to obtain image quality, owns strong noise robust and effects on both FR and NR IQA. Experimental results verify that our model outperforms both current no-reference and full-reference image quality assessment methods on the benchmark screen content image quality assessment database (SIQAD). Cross-database evaluation shows high generalization ability and high effectiveness of our model.     

基于图像质量评价参数的FDST域图像融合

为了提升多源图像融合精度,提出了一种基于图像 质量评价参数的有限离散剪切波变换(FDST)域图像自适应融合方法。利用FDST对源图像进行 多尺度、多方向分解,低频子带图像采用结构相似度与空间频率两种图像评价参数作为系数 权值,高频子带图像应用区域空间频率取大的融合策略。应用有限离散剪切波逆变换(FDSIT )重 构图像。采用多组多源图像进行融合实验,并对融合结果进行了客观评价。实验结果表明, 本文提出的融合方法在主观和客观评价上均优于其他多尺度融合方法,具有更好的融合效果 。     

Reduced reference image quality assessment based on dual derivative priors

A novel reduced reference image quality assessment method is described. Natural images obey very specific distributions in the gradient domain, where some statistical features of the reference image are extracted and sent to the receiver side. The distortion measure for the distorted image is defined by a comparison of these features. The proposed method is generally aimed at all distortion types. Experimental results show that the method performs well compared with other popular methods.     

Perceptual quality assessment for multimodal medical image fusion

Recent years have witnessed that the multimodal medical image fusion (MMIF) plays critical roles in clinical diagnostics and treatment. Many MMIF algorithms have been proposed to improve the MMIF images quality. The quality of multimodal medical fused images will significantly affect the results of the clinical diagnosis. However, little work has been designed to evaluate the effectiveness of MMIF algorithms and the quality of MMIF images. To this end, this paper presents a perceptual quality assessment method for MMIF. A MMIF image database (MMIFID) is first built to employ the classical MMIF algorithms, and the subjective experiment is conducted to assess the quality of each fused image. Then, a no-reference objective method is proposed for the perceptual quality evaluation of MMIF images,which uses Pulse Coupled Neural Network (PCNN) in Non-subsampled Contourlet Transform (NSCT). A fused image is decomposed by NSCT into low frequency sub-band (LFS) and high frequency sub-band (HFS). It is used to motivate the PCNN processing, and large firing times are employed to measure LFS and HFS. Finally, two components evaluation results are combined to obtain the overall objective quality score. Experimental results based on the MMIFID indicate that our presented method outperforms the existing image fusion quality evaluation metrics, and it provides a satisfactory correlation with subjective scores, which shows effectiveness in the quality assessment of medical fused images.     

Screen content image quality assessment based on convolutional neural networks

Screen content image (SCI) is a composite image including textual and pictorial regions resulting in many difficulties in image quality assessment (IQA). Large SCIs are divided into image patches to increase training samples for CNN training of IQA model, and this brings two problems: (1) local quality of each image patch is not equal to subjective differential mean opinion score (DMOS) of an entire image; (2) importance of different image patches is not same for quality assessment. In this paper, we propose a novel no-reference (NR) IQA model based on the convolutional neural network (CNN) for assessing the perceptual quality of SCIs. Our model conducts two designs solving problems which benefits from two strategies. For the first strategy, to imitate full-reference (FR) CNN-based model behavior, a CNN-based model is designed for both FR and NR IQA, and performance of NR-IQA part improves when the image patch scores predicted by FR-IQA part are adopted as the ground-truth to train NR-IQA part. For the second strategy, image patch qualities of one entire SCI are fused to obtain the SCI quality with an adaptive weighting method taking account the effect of the different image patch contents. Experimental results verify that our model outperforms all test NR IQA methods and most FR IQA methods on the screen content image quality assessment database (SIQAD). On the cross-database evaluation, the proposed method outperforms the existing NR IQA method in terms of at least 2.4 percent in PLCC and 2.8 percent in SRCC, which shows high generalization ability and high effectiveness of our model.     

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.     

RRED indices: reduced reference entropic differencing for image quality assessment

We study the problem of automatic "reduced-reference" image quality assessment (QA) algorithms from the point of view of image information change. Such changes are measured between the reference- and natural-image approximations of the distorted image. Algorithms that measure differences between the entropies of wavelet coefficients of reference and distorted images, as perceived by humans, are designed. The algorithms differ in the data on which the entropy difference is calculated and on the amount of information from the reference that is required for quality computation, ranging from almost full information to almost no information from the reference. A special case of these is algorithms that require just a single number from the reference for QA. The algorithms are shown to correlate very well with subjective quality scores, as demonstrated on the Laboratory for Image and Video Engineering Image Quality Assessment Database and the Tampere Image Database. Performance degradation, as the amount of information is reduced, is also studied.     

一种时间优化的全参考图像质量评价算法

针对已有的基于结构相似性的图像质量评价算法的计算复杂的不足,提出一种新的优化算法.针对亮度项中的均值计算,利用积分图像进行的加速计算;针对对比度项和结构项中的方差项计算,利用梯度幅值进行简化计算;最后采用8×8整数窗口逼近多尺度高斯窗口.实验表明,提出算法的评价能力和SSIM相当,运行时间只是SSIM的四分之一.     

夜视融合图像质量主观评价方法

为建立通用、客观的融合图像质量评价方法,在分析图像质量主观评价方法基础上,研究了融合图像质量主观评价对象、评价条件、评价指标和数据处理等关键环节,结合目标探测识别与场景理解两个典型的视觉任务,提出了目标可探测性和细节可分辨性两个主观评价指标,并研究了图像整体感知质量与这两个指标的相关性。对3种不同场景的微光与红外融合图像,采用9种融合方法获得的189幅融合图像的统计分析结果显示,细节可分辨性和图像整体感知质量相关性好,而目标可探测性虽然和图像整体感知质量相关性较差,但在基于具体视觉任务的融合图像质量评价过程中,仍可作为有效评价指标之一。     

夜视融合图像质量客观评价方法

为建立通用、客观的融合图像质量评价方法,在分析图像质量评价与融合图像质量评价关系基础上,给出了图像质量评价与融合图像质量评价的一般表达式。依据信息理论和结构相似度评价方法,对建立的4种客观评价指标,采用4种融合方法获得的36幅融合图像进行了主观评价实验,统计分析结果显示,结合人类视觉系统的客观评价方法优于熵、交互信息量等评价指标,但仍未达到高度的主客观一致性,说明构建通用、高效、主客观一致性好的融合图像质量评价指标存在较大难度,同时对可能存在的原因进行了分析。