Image quality assessment based on S-CIELAB model

This paper proposes a new image quality assessment framework which is based on color perceptual model. By analyzing the shortages of the existing image quality assessment methods and combining the color perceptual model, the general framework of color image quality assessment based on the S-CIELAB color space is presented. The S-CIELAB color model, a spatial extension of CIELAB, has an excellent performance for mimicking the perceptual processing of human color vision. This paper incorporates excellent color perceptual characteristics model with the geometrical distortion measurement to assess the image quality. First, the reference and distorted images are transformed into S-CIELAB color perceptual space, and the transformed images are evaluated by existing metric in three color perceptual channels. The fidelity factors of three channels are weighted to obtain the image quality. Experimental results achieved on LIVE database II shows that the proposed methods are in good consistency with human subjective assessment results.     

Pair comparison based progressive subjective quality ranking for underwater images

Underwater images contain an interacting mixture of distortions due to the physicochemical properties of the water, suspended organic matter and floating particles in water. Unlike images in traditional natural image quality databases, underwater images are often difficult to acquire with reference images and sets of images with gradient distortion. Therefore, it is even more difficult for the viewers to assign an absolute psychophysical scale to the quality of underwater images. In this paper, we propose a pairwise subjective comparison procedure for underwater images quality ranking inspired by the intuitive suppression and competence mechanisms in visual perception. In the proposed method, we construct a preselection based initial image quality dataset by full pairwise comparison, which also enables online adaptive new image updating. The proposed method is not constrained by the lack of reference images, and is reliable and sensitive to images with discriminable distortion level and various image contents. The proposed pairwise comparison further allows an uncertain choice, which does not require a reinforce human opinion. To the best of our knowledge, this is the first implementation for underwater image subjective quality ranking, and a new approach to the image quality ranking for different image contents with unknown distortion level. We demonstrate that the obtained subjective image ranking correlates well with the human perception of quality difference among the underwater images than that of the single stimuli image quality assessment with finite labor burden. Moreover, our proposed method accurately characterize the gradual degradation in the underwater image sequence taken in controlled conditions. The proposed progressive learning ranking is also an alternative way to realize adaptive extension of the existing image quality databases.     

融合深度学习与多尺度Retinex的水下图像增强方法

针对水下图像纹理模糊和色偏严重等问题,提出了一种融合深度学习与多尺度导向滤波Retinex的水下图像增强方法。首先,将陆上图像采用纹理和直方图匹配法进行退化,构建退化水下图像失真的数据集并训练端到端卷积神经网络(convolutional neural network,CNN) 模型,利用该模型对原始水下图像进行颜色校正,得到色彩复原后的水下图像;然后,对色彩复原图像的亮度通道,采用多尺度Retinex(multi-scale Retinex,MSR) 方法得到纹理增强图像;最后,融合色彩复原图像中的颜色分量和纹理增强图像得到最终水下增强图像。本文利用仿真水下图像数据集和真实水下图像对提出方法进行性能测试。实验结果表明,所提方法的均方根误差、峰值信噪比、CIEDE2000和水下图像质量评价指标分别为0.302 0、17.239 2 dB、16.878 4和4.960 0,优于5种对比方法,增强后的水下图像更加真实自然。本文方法在校正水下图像颜色失真的同时,能有效提升纹理清晰度和对比度。     

基于Retinex的低照度图像增强

Retinex理论是颜色恒常知觉的计算理论,可以用于图像清晰度严重失真状况下的图像增强.在研究Retinex算法的基础上,对低照度彩色图像失真中色彩恢复存在的问题进行研究,构建了一个恢复效果较好的色彩恢复函数——余弦色彩恢复函数.给出了处理后图像的自动补偿/增益方法以及补偿/增益中参数实际选取的经验值.对处理后的图像进行了质量评价分析,表明该恢复函数在处理低照度图像时具有较明显的改善效果.     

一个图像质量盲评估的统计测度

本文提出了一个图像质量盲评估的统计测度.该测度首先根据自然图像的统计性质与失真图像的模型,实现对图像小波系数分布参数的盲估计;再利用估计的分布参数来计算失真图像与参考图像之间的互信息,以量化失真图像对参考图像的保真度,进而实现对图像质量的评估.本文提出的测度避免了对参考图像的依赖,且克服了现有图像质量盲评估对特征选择与提取、机器学习等过程的依赖.LIVE图像质量评估数据库的总体评估结果表明：本文提出的盲评估统计测度对图像质量评估结果与数据库的主观评估结果高度一致,且优于文献中报道的盲评估测度.     

The chordlet transform with an application to shape compression

Due to their abilities to succinctly capture features at different scales and directions, wavelet-based decomposition or representation methods have found wide use in image analysis, restoration, and compression. While there has been a drive to increase the representation ability of these methods via directional filters or elongated basis functions, they still have been focused on essentially piecewise linear representation of curves in images. We propose to extend the line-based dictionary of the beamlet framework to one that includes sets of arcs that are quantized in height. The proposed chordlet dictionary has elements that are constrained at their endpoints and limited in curvature by system rate or distortion constraints. This provides a more visually natural representation of curves in images and, furthermore, it is shown that for a class of images the chordlet representation is more efficient than the beamlet representation under tight distortion constraints. A data structure, the fat quadtree and an algorithm for determining an optimal chordlet representation of an image are proposed. Codecs have been implemented to illustrate applications to both lossy and lossless low bitrate compressions of binary edge images, and better rate or rate–distortion performance over the JBIG2 standard and a beamlet-based compression method are demonstrated.     

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.     

基于非负矩阵分解的彩色图像质量评价方法

针对稀疏表示的图像质量评价模型都基于灰度图像,缺少颜色信息,该文提出一种基于非负矩阵分解(NMF)的全参考彩色图像质量评价方法。首先,从自然彩色图像中随机采样,得到训练样本,利用非负矩阵分解,训练得到特征基矩阵,并经过Schmidt正交化,构建特征提取矩阵;其次,根据视觉显著性模型,利用最大视觉显著性和显著性差值两步骤选取视觉重要区域;最后,利用特征提取矩阵,得到低维的特征向量,并最终得到彩色图像质量评价值。实验结果表明,该文方法在LIVE, CSIQ和TID2008 3个图像质量评价库上有很好的表现。3个图像库的平均结果显示,该文方法的综合表现优于所有对比方法。这表明该文方法与主观感知有更好的关联度。     

基于结构信息和像素失真的客观图像质量评价

为了度量多种失真类型的图像质量,根据人类视觉系统(HVS)对图像空域结构信息高度敏感和任一类型的失真都会产生像素失真理论,提出一种基于结构信息和像素失真的无参考的质量评价方法.该方法利用色彩信息提取能够表征图像结构信息的视觉内容结构图,并加权像素失真来度量图像质量,同时对部分失真类型进行修正.该方法不涉及任何参数设置也无需训练过程.实验结果表明,该方法能够较好地评价白噪声、JPEG压缩、高斯模糊、JPEG2000压缩和FastFading等失真图像的质量,并与主观评价方法有较好的一致性.     

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.     

针对对比度变化的图像质量评价方法

由于对比度变化容易引入图像亮度和色彩等失真,本文提出了一种面向对比度变化的图像质量评价方法CCIQA。所提方法先将图像进行亮度和色度分离,再分别根据亮度强度变化和明暗对比度变化提取亮度失真因子和根据色度相似性提取色度失真因子,接着依照基于亮度强度的权重图进行融合并计算得到最终图像质量评价分数。所提CCIQA方法在4个常用的数据库,TID2008,TID2013,CID2013和CCID2014进行广泛测试。实验结果表明所提CCIQA算法符合人眼视觉对对比度变化的主观感知,且算法性能优于多个最新图像质量评价方法。      

基于蓝绿通道自适应色彩补偿的水下图像增强

光在水中传播时受到水的吸收和悬浮粒子散射作用,导致水下图像颜色失真、对比度低、可视性差。针对上述退化问题,该文提出一种基于蓝绿通道自适应色彩补偿水下图像增强方法。首先,该方法分析水下成像模型的特点,根据蓝、绿色通道均值在3通道均值和的占比,将水下场景深度划分3个等级,利用光衰减率特性自适应补偿色彩,实现多场景色彩校正。然后对色彩补偿后的图像划分暗调、中间暗调、中间亮调、亮调4个区域,利用暗区域映射函数将图像暗区域映射到亮区域,在提升对比度的同时抑制噪声的产生。最后采用双线性插值解决分块处理产生的区域块效应。真实水下数据集实验结果表明,与现有方法相比,该方法可以提升多种场景的水下图像质量。     

Previsualized image vector quantization with optimized pre- andpostprocessors

The optimal previsualized image vector quantization method for compressing digital images to a bit rate of 0.75 bpp or below with moderately low to very low subjective distortion is presented. The encoding method incorporates a visual model as part of the distortion measure. By modeling the quantization noise as an additive signal-dependent noise process, an optimum pre- and postprocessing system, which minimizes the mean-squared error measured inside the visual model, is derived. The analysis of the system performance and a coordinate descent design algorithm are discussed. A set of experiments was conducted using the optimum system, and the results were compared to those obtained by other methods. The study shows that the images quantized by the method presented exhibit much less sawtooth, blocking, and contouring effects and higher subjective quality. Images of surprising quality have been produced by this method at a bit rate of about 0.1 bpp with a compression ratio of 80:1 relative to a normal 8 bpp original     

Reduced Reference Quality Metric for Synthesized Virtual Views in 3DTV

Multi‐view video plus depth (MVD) has been widely used owing to its effectiveness in three‐dimensional data representation. Using MVD, color videos with only a limited number of real viewpoints are compressed and transmitted along with captured or estimated depth videos. Because the synthesized views are generated from decoded real views, their original reference views do not exist at either the transmitter or receiver. Therefore, it is challenging to define an efficient metric to evaluate the quality of synthesized images. We propose a novel metric—the reduced‐reference quality metric. First, the effects of depth distortion on the quality of synthesized images are analyzed. We then employ the high correlation between the local depth distortions and local color characteristics of the decoded depth and color images, respectively, to achieve an efficient depth quality metric for each real view. Finally, the objective quality metric of the synthesized views is obtained by combining all the depth quality metrics obtained from the decoded real views. The experimental results show that the proposed quality metric correlates very well with full reference image and video quality metrics.     

基于背景光修正成像模型的水下图像复原

光在水下传播时由于受到水体吸收和散射作用的影响,导致水下图像质量严重退化。为了有效去除色偏和模糊,改善水下图像质量,该文提出一种基于背景光修正成像模型的水下图像复原方法。该方法基于对雾天图像的观察,提出了水下图像背景光偏移假设,并基于此建立背景光修正成像模型;随后使用单目深度估计网络获得场景深度的估计,并结合背景光修正的水下成像模型,利用非线性最小二乘拟合获得水下偏移分量的估计值从而实现水下图像去水;最后优化去水后的含雾图像的透射率,并结合修正后的背景光实现图像复原。实验结果表明,该文方法在恢复水下图像颜色和去除散射光方面效果良好。     

基于Contourlet变换的质降参考图像质量评价模型

提出了一种新的基于Contourlet变换的质降参考图像质量评价模型(RR-CTBM),通过衡量图像间的纹理相似度,达到质量度量的目的。实验结果表明,所提出的RR-CTBM与主观视觉感知具有较好的一致性。     

Perceptual quality evaluation for image defocus deblurring

Blur is one of the most common distortion types in image acquisition. Image deblurring has been widely studied as an effective technique to improve the quality of blurred images. However, little work has been done to the perceptual evaluation of image deblurring algorithms and deblurred images. In this paper, we conduct both subjective and objective studies of image defocus deblurring. A defocus deblurred image database (DDID) is first built using state-of-the-art image defocus deblurring algorithms, and subjective test is carried out to collect the human ratings of the images. Then the performances of the deblurring algorithms are evaluated based on the subjective scores. With the observation that the existing image quality metrics are limited in predicting the quality of defocus deblurred images, a quality enhancement module is proposed based on Gray Level Co-occurrence Matrix (GLCM), which is mainly used to measure the loss of texture naturalness caused by deblurring. Experimental results based on the DDID database demonstrate the effectiveness of the proposed method.     

基于小波分析的图像稀疏保真度评价

该文针对传统的图像质量评价方法无法有效模拟人类视觉系统(HVS)存在的不足,提出基于小波分析的加权稀疏保真度(Weighting Sparse Fidelity, WSF)图像评价算法。算法以模拟人类视觉系统的神经网络为切入点,对图像进行一阶小波分解得到4个不同方向的子带图像,然后将子带图像分成88大小的图像块,采用快速独立分量分析(FastICA)的方法对各个图像块进行训练并提取图像特征检测矩阵,根据特征检测矩阵计算各子带图像块的稀疏特征值并建立稀疏保真度质量评价模型。在此基础上,根据细节信息的不同对低频子带图像进行区间划分并设置视觉权重,使之更加接近人眼的主观视觉。实验中对LIVE库中所有图像进行算法验证,其结果表明,所提方法能很好地对各种失真类型的图像进行评价。基于小波分析的稀疏保真度评价算法能够有效模拟人类视觉系统的多频特性和视觉皮层感知机制,弥补现有图像质量评价方法在此方面的不足。     

基于SEM的彩色融合图像质量主观评价方法北大核心CSCD

作为客观评价的研究基础,人眼主观评价是比较不同融合算法图像优劣的最直接有效的方法。通过开展主观评价实验,基于单因变量模型分析清晰度、颜色协调性等影响因素对彩色融合图像质量的影响程度,进而预测视觉任务下的融合图像总体质量,是目前流行的彩色融合图像质量主观评价研究方法。针对基于单因变量模型的研究方法易割裂“探测”与“感知”在探测识别过程中的联系的问题,本文以村庄环境下的探测识别视觉任务为例,选取“感知”和“探测”2个指标共同作为彩色融合图像质量的表现指标,组织15人对由8个场景、8种典型融合算法生成的42幅彩色融合图像的质量进行了主观评价。采用双因变量的结构方程模型(Structural Equation Model,SEM)分析了筛选后的评价结果,建立了彩色融合图像质量的预测模型并验证了模型的正确性。结果表明,当同时表现“感知”和“探测”2种性能时,村庄环境、探测识别视觉任务下,彩色融合图像的质量可由“目标背景差异”、“场景清晰度”和“颜色协调性”3个因素预测,且“感知”和“探测”2种性能是均衡的。     

基于稀疏表示的立体图像客观质量评价方法

提出了一种基于稀疏表示的立体图像质量评价方法 ,分为训练和测试两个部分。在训练部 分,通过训练不同频带的立体图像获得立体图像的稀疏字典;在测试部分,根据稀疏字典计 算得到立体图 像的稀疏特征,定义了稀疏特征相似度衡量原始和失真图像信息的差异,并根据稀疏字典计 算了频带增益和左右视点的融合权值,最后融合稀疏特征相似度作为立体图像质量的 客观评价值。在立体图像测试库上的实验结果表明,本文方法的评价结果与主观评价结果有 较好的相关性,符合人类视觉系统的感知。