基于人类视觉系统的融合图像质量评价方法的研究

在融合图像的质量评价方法中引入人眼的各种视觉特性,从而使客观计测的结果符合图像质量的主观评价,符合人眼的实际观看质量.讨论了一种新的基于人类视觉系统(HVS)的融合图像质量度量,该方法无需理想图像,同时,将质量评价与人的视觉结果直接关联.对多类图像的不同融合算法的质量评价表明该方法是一种实用、有效的图像融合质量评价方法.     

一种基于局部方差和结构相似度的图像质量评价方法

针对结构相似度(SSIM)方法对图像不同失真类型的敏感程度不同的问题,提出了一种基于局部方差和结构相似度的图像质量评价方法.计算并比较了两图像整体局部方差的分布,分析了度量结果所表征的图像特征.局部方差较好地体现了图像的细节信息,因此对于模糊等失真类型较为敏感,但是对于噪声污染等失真类型的敏感程度较低.因此,本文提出的评价方法将局部方差与结构相似度方法相结合,平衡了二者对造成图像失真各种因素的不同敏感偏向,从而使评价结果更加接近人眼对图像质量的主观感觉.实验结果表明,该方法充分考虑了图像中人跟比较敏感的细节信息的完整程度对图像质量的影响,评价结果优于SSIM方法,与人的主观感觉的一致性较好.     

CMMB图像质量主观评价方法研究

通过分析目前国内外视频图像质量评价的相关标准,并结合中国移动多媒体广播(CMMB)系统的应用特性,对图像质量主观评价方法进行深入分析,提出了在进行CMMB图像质量主观评价过程中需要考虑的主要问题。在此基础上,进一步设计并实现对CMMB图像质量的主观评价,并对评价结果进行了统计分析,以验证评价方法的可靠性。     

一种基于WBCT的自然图像质量评价方法

部分参考型图像质量评价的性能依赖于图像的特征提取和稀疏表示.WBCT(Wavelet-based Contourlet Transform)是一种无冗余度的图像稀疏表示方法,能有效地反映图像的视觉感知特性,可以用来捕捉由于图像失真而引起的视觉感知的变化.为此,结合部分参考型图像质量评价模型和WBCT,提出了一种自然图像质量客观评价方法.实验结果表明,利用该方法所得到的图像质量客观评价结果与主观评价结果之间具有很好的一致性,能准确地反映人眼对图像质量的主观感受.     

帧频和响应时间对移动显示LCD图像质量的影响

应用视觉感知实验的方法对移动显示(手机)用LCD的运动图像质量进行了主观评价,研究了帧频、响应时间和视频图像内容对运动图像质量的影响.通过对实验数据的统计分析,发现三者对视觉感知的运动图像质量均有统计学意义的影响.帧频较高的运动图像质量较好,且存在一个最优的频帧;响应时间较长的运动图像质量较差;而视频图像内容不同,被感知的运动伪像也不同,模糊和跳跃是最容易被感知的运动图像失真.     

基于波前像差的图像质量评价方法研究

图像质量评价的研究已成为图像信息工程的基础技术之一。由于图像的最终接受者是人,所以评价图像质量应反映出人类的主观视觉感知。为构造一种符合人眼视觉特性的图像质量评价方法,利用点扩散函数针对人眼建立了含有波前像差信息的图像视觉评价模型,并用此模型分别对添加不同噪声的图像进行图像质量评价。实验结果表明,该方法是可行的、有效的,不同的人眼对同一幅图像的评价存在有差异,人眼波前像差越小观察到的图像越清晰。该方法不仅能够在评价图像质量时准确反映人眼的主观感知,而且能够直观地呈现不同人眼实际看到的图像。     

移动显示液晶屏上视频图像质量研究

在较低帧率下,移动显示液晶屏上主观图像质量与视频图像内容相关。文章提出一种新的视频图像内容运动特征提取方法,即首先计算每帧视频图像的运动矢量场,通过进一步计算每帧运动矢量的熵值和均值来描述和量化视频图像内容的运动特征。采用主观视觉实验研究了不同帧率下不同视频图像内容的主观图像质量。实验数据分析表明,运动矢量熵值和均值与液晶显示屏上运动图像模糊和运动不连续等显示失真现象具有较高的相关性。本文提出的视频图像内容运动特征提取方法为液晶显示屏视频驱动方法的优化和改进提供了依据,并且其研究结果可应用于视频图像内容提取的相关算法中。     

典型场景彩色融合图像颜色协调性的主观评价

彩色夜视技术是当前夜视技术发展的前沿,但如何实现对彩色夜视图像质量的客观评价仍然是当前困扰人们的问题.针对北京理工大学高绍姝提出的颜色协调性模型主观评价实验人数不足的问题,按照国际视觉实验标准重新设计并进行了颜色协调性的主观视觉评价实验,主观评价测试的彩色融合图像分成绿色植物、海天和城镇建筑物三类典型场景,主观实验人数提高到52人;对主观评价实验结果的回归分析结果表明:高绍姝提出的颜色协调性评价模型在三类场景与主观评价结果均具有较好的一致性,明显优于其他评价模型,可以有效地对不同场景彩色融合图像的颜色协调性进行客观评价.     

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。     

基于小波第二级系数误差的图像质量评价模型

图像质量研究的目标是设计客观评价算法,它与主观评价有很强的相关性.本文根据人眼对图像高低频失真的敏感度不同,采用不同方法分别计算图像高频和低频失真量,然后将两种失真量直接相乘得到客观评价值.实验结果表明,这种客观评价模型能够一致评价各种失真类型、各种失真强度的图像质量,与LIVE图库上差异主观评价分(DMOS)的线性相关性优于PSNR、LMSE和SSIM算法.     

Spatial pooling for measuring color printing quality attributes

Many objective image quality assessment algorithms firstly apply quality metrics in local regions that results in a quality map, and then pool the quality values in the quality map into a single quality score. The simplest pooling method is the average of quality values, which assumes that all the quality values are independent and equally important. However, visual perception is so complex that the assumption underlying average pooling might be too strict. There is an agreement that some regions in the images might be more perceptually significant, which leads to more advanced spatial pooling methods. In this work we evaluate existing spatial pooling methods for five important quality attributes, which are proposed to reduce the complexity of image quality assessment. The results show that: (1) more advanced spatial pooling methods are generally better than simple average; (2) spatial pooling depends on both image quality metrics and the attributes of the image.     

Image fusion using higher order singular value decomposition

A novel higher order singular value decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.     

基于边缘特征和亮度的彩色图像质量评价

传统的图像质量评价算法大多针对灰度图像而言,这些算法利用图像中对应的像素灰度误差建立数学模型来评价图像,不能够评价彩色图像的质量。本文提出了一种基于边缘特征和颜色亮度信息的彩色图像质量评价方法。首先,利用Sobel算子提取图像边缘;其次,定义了边缘和亮度相似系数来分析它们对图像的影响。最后,综合考虑这两个因素建立数学模型对彩色图像进行评价。大量实验表明:该方法适合评价彩色图像;同时,该实验结果与人的主观感觉的一致性较好。     

Image aesthetic quality evaluation using convolution neural network embedded learning

A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.     

Contact Imaging: Simulation and Experiment

We report simulated and experimental image quality for contact imaging, a method for imaging objects close to the sensor surface without intervening optics. This technique preserves microscale resolution for applications that can not tolerate the size or weight of conventional optical elements. In order to assess image quality, we investigated the spatial resolution of contact imaging, which depends on the sensor size as well as the distance between objects and the sensor surface. We studied how this distance affects image quality using a commercial optical simulator. Simulation results show that the image quality degrades as objects move away from the sensor surface. To experimentally validate these results, an image sensor was designed and fabricated in a commercially available three metal, two poly, 0.5 mum CMOS technology. Experiments with the contact imager corroborate the simulation results. Two specific applications of contact imaging are demonstrated.     

Fast 2-D distance transformations

The performance of a number of image processing methods depends on the output quality of a distance transformation (DT) process. Most of the fast DT methodologies are not accurate, whereas other error-free DT algorithms are not very fast. In this paper, a novel, fast, simple, and error-free DT algorithm is presented. By recording the relative x- and y-coordinates of the examined image pixels, an optimal algorithm can be developed to achieve the DT of an image correctly and efficiently in constant time without any iteration. Furthermore, the proposed method is general since it can be used by any kind of distance function, leading to accurate image DTs.     

应用相位一致性评价多光谱遥感图像条带噪声

条带噪声是影响多光谱遥感图像质量的重要因素之一,严重影响遥感数据的解译和信息提取。提出一种应用相位一致性进行多光谱遥感图像条带噪声质量评价的方法。该方法不受图像亮度或对比度变化的影响,以频域中相位一致性探测图像特征点,综合考虑图像中条带噪声的数量、长度、宽度和强度建立评价因子,客观评价遥感图像中存在的条带噪声对图像质量的影响。实验证明该方法评价结果符合人眼视觉感官评价。     

用于荧光显微镜的正交偏振滤波图像增强技术

在荧光显微镜中,微弱的荧光信号一般淹没于较强的激发光中,显微镜的成像质量在很大程度上取决于提取微弱荧光信号的能力。目前,荧光显微镜均根据荧光与激发光波长的差异,采用频率滤波法滤除激发光,实现图像增强。但该方法不仅对滤光片要求高,而且对荧光和激发光的波长有严重的依赖性。基于激发光与荧光在偏振态上的差异,提出了一种用于荧光显微镜的正交偏振滤波图像增强技术。研究表明,正交偏振滤波图像增强技术能够显著地提高成像质量,对光学元件性能参数的要求大幅度降低。丰富了从强激发光中提取弱荧光信号的技术手段,为今后解决波长可调谐的多光谱荧光显微镜、白光照明多光谱荧光显微镜等技术上的瓶颈提供了参考。     

No-reference assessment of blur and noise impacts on image quality

The quality of images may be severely degraded in various situations such as imaging during motion, sensing through a diffusive medium, and low signal to noise. Often in such cases, the ideal un-degraded image is not available (no reference exists). This paper overviews past methods that dealt with no-reference (NR) image quality assessment, and then proposes a new NR method for the identification of image distortions and quantification of their impacts on image quality. The proposed method considers both noise and blur distortion types that may exist in the image. The same methodology employed in the spatial frequency domain is used to evaluate both distortion impacts on image quality, while noise power is further independently estimated in the spatial domain. Specific distortions addressed here include additive white noise, Gaussian blur and de-focus blur. Estimation results are compared to the true distortion quantities, over a set of 75 different images.