基于膨胀的梯度结构相似度图像质量评价方法

传统的梯度结构相似度算法(GSSIM)简单地将各子块GSSIM的平均值作为整幅图像的质量评估值,忽略了人眼对图像不同失真区域的视觉灵敏度不同的特点。针对此问题,提出了一种基于膨胀和图像块分类的加权梯度结构相似度图像质量评价方法(WGSSIM)。该方法首先将失真图像划分为两个区域:边缘膨胀区域和平滑区域;然后将失真图像划分成8×8的图像块,根据失真区域将图像块区分为边缘膨胀块与平滑块两类;最后对不同类型图像块之间的GSSIM值赋予不同的权值,计算得到整幅图像的WGSSIM。实验表明,该方法在3个数据库上的评价结果稳定、合理,更加符合人眼视觉系统特性,评价结果与主观评价有很好的一致性。     

改进的基于MRF原理的块效应消除算法

基于块的DCT变换是现在应用最广泛的图像与视频压缩变换方法之一,在很多具有重要影响力的图像压缩标准如MPEG、JPEG、H.261中得到应用。通过BDCT变换,可以获得较高的图像压缩率,但是BDCT变换也会损害图像质量,其中块效应就是常见的现象。现存的块效应消除算法或模糊了图像的细节,或达不到较理想的块效应消除效果。在兼顾消除块效应和保护图像细节部分的原则上,将分区思想应用于基于图像马尔可夫随机场（MRF）原理的块效应消除算法中,提出了针对图像边界、纹理区域、平坦区域以及分块边界选择不同的MRF势函数的方法。同时,根据图像的块效应产生的灰度值偏差分布的统计分析结果改进了MRF模型的求解算法,简化了求解过程。对已有的边界检测和图像分区算法进行了改进,应用快速算法,简化了其中对块效应消除结果影响很小的计算过程。实验结果表明,这种算法既保护了图像的细节信息,又达到了良好的消除块效应的效果,并且具有较好的时间效率。     

基于感兴趣区域的边缘结构相似度图像质量评估

目前常用的评估算法忽视了图像中携带重要信息的边缘特征。基于感兴趣区域的双尺度边缘结构相似度算法虽然考虑了边缘信息的重要性,但对边缘信息的识别效果不理想。针对以上问题,提出边缘结构相似度图像质量评估算法,将图像分为感兴趣区域与不感兴趣区域,分别用边缘结构相似度算法和结构相似度算法对2种区域进行质量评估,并实现加权组合。实验结果表明,该算法对边缘信息的识别能力更强,对图像质量的变化更加敏感。     

一种块效应图像的处理方法

DCT变换以其优良特性被图像和视频的数据压缩编码所广泛采用。通常先把图像分成8×8的块,然后再对图像块做DCT变换,之后再对DCT系数进行量化,以压缩掉视觉不敏感的高频信号。而粗糙的量化常常会导致解码图像产生块效应、振铃现象等问题,从而影响图像的视觉质量。在分析块效应产生原因的基础上,着重介绍了一种有效改善块效应图像质量的处理方法。由于块效应图像在不同的区域有不同的表现形式,因此可以根据区域各自的纹理特点采取相应的处理方法。实验证明,这种处理方法能够有效消除块效应,同时对图像的亮度和细节损失不大。     

基于掩盖效应的无参考数字图像质量评价

针对数字图像的客观质量评估，从噪声检测的角度，提出了一种基于掩盖效应的无参考图像质量评估方法。首先对Hosaka分块进行了改进，取消了该方法对图像尺寸的限制。通过分块，将图像以不同的频率成分区分开来。然后计算各个子块的噪声。根据图像的污染程度，提出了基于掩盖效应的无参考图像峰值信噪比NPSNR。实验结果表明，该方法具有无参考、运算复杂度低、主客观较一致等特点。     

基于马尔可夫随机场最大后验估计的去块效应方法

基于块离散余弦变换(BDCT)是国际上大多数图像、视频压缩标准的核心部分,它的主要缺点是在低比特率时其恢复图像的块边界上会出现明显可见的方块效应,降低了图像的视觉质量。文章提出了一种新的基于马尔可夫随机场最大后验估计(MRF-MAP)的块效应消除算法,目的是实现在尽量消除块效应的同时充分地保护图像的边缘信息。文中通过线性回归,给出了设置MRF算法中的Huber函数阈值的数学公式,由该公式得到的阈值可以在保护图像边缘和提高图像质量之间有较好的折中。仿真结果验证了该文算法的有效性。     

一种块效应图像的处理方法

DCT变换以其优良特性被图像和视频的数据压缩编码所广泛采用.通常先把图像分成8×8的块,然后再对图像块做DCT变换,之后再对DCT系数进行量化,以压缩掉视觉不敏感的高频信号.而粗糙的量化常常会导致解码图像产生块效应、振铃现象等问题,从而影响图像的视觉质量.在分析块效应产生原因的基础上,着重介绍了一种有效改善块效应图像质量的处理方法.由于块效应图像在不同的区域有不同的表现形式,因此可以根据区域各自的纹理特点采取相应的处理方法.实验证明,这种处理方法能够有效消除块效应,同时对图像的亮度和细节损失不大.     

一种图像校正重采样效应的评估及优化方法

分析了图像校正中重采样效应的主要表现形式，建立了一种重采样效应的评估方法．在此基础上，根据图像投影变换及校正图像上基础矩阵的性质，给出了两种类型的图像校正不变量，提出了一种独立于图像校正过程的重采样效应优化方法，通过投影区域修正的方法提高校正图像质量，实验证明，该方法能够有效的降低图像变形，减少图像分辨率的损失．     

基于边缘和局部熵融合的视觉安全性评估方法

针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性，引入图像的边缘特征，通过共有边缘来衡量加密图像与原始图像的边缘相似度，消除了块效应。由于局部熵对加密等级低的图像不敏感，边缘相似度对加密等级高的图像不敏感，将两个评估方法进行自适应融合，提出[SLEES]（Local Entropy and Edge Similarity，[SLEES]）指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试，实验结果表明，[SLEES]指标相比传统评估指标有更好的鲁棒性，评估范围更广。     

全相位DCT自适应插值算法

全相位DCT内插方法可有效减少吉布斯效应,具有优良的内插特性。在此基础上,结合边缘自适应内插思想,提出全相位DCT自适应内插算法,并根据插值点邻近像素采样点的不同选取方法,推导出相应的内插公式。实验结果表明,提出的全相位DCT自适应内插算法在有效抑制吉布斯效应的同时,能更好地保持图像边缘,改善图像质量。     

一种基于内容的图像质量评价测度

鉴于传统的图像质量评价测度,如峰值信噪比,不能有效地反映人对图像的视觉感知。为此,提出了一种基于内容的图像质量评价测度;在改进基于结构相似度(structural similarity,SSIM)的图像质量测度基础上,根据图像的内容将图像分成边缘、纹理和平滑区域3部分,在每个区域又利用模糊积分融入了结构相似性的数量信息,从而充分利用了图像结构信息相似性及其在位置和数量上的融合信息来全面评价图像质量。实验结果表明,利用该测度所得到的图像质量评价结果与主观评价结果有着很好的相关性,能较准确地反映人对图像质量的主观感受。     

一种基于图象小波极值表示的减少方块效应的算法

针对低比特率JPEG压缩图象会产生严重的方块效应问题和根据后处理技术可有效地减少方块效应的认识,提出了一种新的基于图象小波极值描述的后处理算法。该算法根据解压缩图象方块效应在一级和二级小波变换系数中的极值特性,对位于图象背景区、平滑边缘区及阶梯状边缘区的方块边界的小波极值分别加以处理。实验结果表明,该算法能有效地减少方块效应,且能改善译码图象的信噪比和主观视觉质量。     

适应视觉感知的块效应消除算法

针对编码图像出现块效应的现象,提出一种以人类视觉感知驱动的块效应消除算法。传统块效应消除算法经常会导致图像边缘丢失,为了解决这个问题,本文对块效应影响像素应用自适应滤波器和模糊推理技术。仿真结果表明算法可以获得比传统算法更好的效果。     

No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images

In this paper, we propose a new no-reference image quality assessment for JPEG compressed images. In contrast to the most existing approaches, the proposed method considers the compression processes for assessing the blocking effects in the JPEG compressed images. These images have blocking artifacts in high compression ratio. The quantization of the discrete cosine transform (DCT) coefficients is the main issue in JPEG algorithm to trade-off between image quality and compression ratio. When the compression ratio increases, DCT coefficients will be further decreased via quantization. The coarse quantization causes blocking effect in the compressed image. We propose to use the DCT coefficient values to score image quality in terms of blocking artifacts. An image may have uniform and non-uniform blocks, which are respectively associated with the low and high frequency information. Once an image is compressed using JPEG, inherent non-uniform blocks may become uniform due to quantization, whilst inherent uniform blocks stay uniform. In the proposed method for assessing the quality of an image, firstly, inherent non-uniform blocks are distinguished from inherent uniform blocks by using the sharpness map. If the DCT coefficients of the inherent non-uniform blocks are not significant, it indicates that the original block was quantized. Hence, the DCT coefficients of the inherent non-uniform blocks are used to assess the image quality. Experimental results on various image databases represent that the proposed blockiness metric is well correlated with the subjective metric and outperforms the existing metrics.

     

一种基于模糊逻辑的图像多层次增强算法

在分析图像传统单层次模糊增强算法（Pal算法）的基础上，为克服Pal算法仅增强图像阈值参数附近某一部分边缘的不足。提出一种新的基于模糊逻辑的图像多层次增强算法。新算法通过引入模糊熵，进行有目的选取不同灰度层次的阈值，同时增强图像中不同灰度层次的边缘信息，另外新算法对Pal算法中的复杂变换进行了简化，采用了新的增强算子，克服了Pal算法速度慢且损失图像部分灰度信息的缺陷。将新算法应用于图像边缘提取中，取得了优于基于传统单层次模糊增强算法进行图像边缘提取的效果。     

No-reference image quality assessment using interval type 2 fuzzy sets

Image quality assessment of distorted or decompressed images without any reference to the original image is challenging from computational point of view. Quality of an image is best judged by human observers without any reference image, and evaluated using subjective measures. The paper aims at designing a generic no-reference image quality assessment (NR-IQA) method by incorporating human visual perception in assigning quality class labels to the images. Using fuzzy logic approach, we consider information theoretic entropies of visually salient regions of images as features and assess quality of the images using linguistic values. The features are transformed into fuzzy feature space by designing an algorithm based on interval type-2 (IT2) fuzzy sets. The algorithm measures uncertainty present in the input–output feature space to predict image quality accurately as close to human observations. We have taken a set of training images belonging to five different pre-assigned quality class labels for calculating foot print of uncertainty (FOU) corresponding to each class. To assess the quality class label of the test images, maximum of T-conorm applied on the lower and upper membership functions of the test images belonging to different classes is calculated. Our proposed image quality metric is compared with other no-reference quality metrics demonstrating more accurate results and compatible with subjective mean opinion score metric.     

图像分割质量评价的二型模糊集方法

图像分割质量的评价是图像分割技术和算法研究的重要环节,在图像分析和计算机视觉中有着重要应用。依据二型模糊集在不精确性描述方面的独特优势,提出一种图像分割评判指标的二型模糊集表示方法,引入两种二型模糊集的模糊性度量作为图像分割质量的评判标准,构建图像分割质量评价模型。模拟实验验证了该模型的有效性和实用性。     

Objective image quality measurement by local spatial‐frequency wavelet analysis

An essential determinant of the value of surrogate digital images is their quality. Image quality measurement has become crucial for most image processing applications. Over the past years, there have been many attempts to develop models or metrics for image quality that incorporate elements of human visual sensitivity. However, there is no current standard and objective definition of spectral image quality. This paper proposes a reliable automatic method for objective image quality measurement by local spatial‐frequency wavelet analysis. The analysis is performed locally by dividing an image into 64×64 pixel blocks, and transforming the data into wavelet domain and sub‐band image domain. A fast lifting wavelet algorithm is developed for computationally efficient spatial‐frequency deconstruction of images to extract the features of edges in sub‐band datasets. Wavelet analysis throughout the spatial‐frequency range with respect to noise, sharpness, brightness, contrast, and modulation transfer function (MTF) reveals more detailed information to relate the quality of an image to the interpretation by using an artificial neural network to optimize weightings and results in a promising quantitative measure of image quality. Experimental results from using this method for image quality measurement exhibit good correlation to subjective visual quality assessments. The proposed approach with fast computation provides a potential metric for reliable image quality quantification, allowing image quality to be automatically evaluated following image capture/compression, but prior to permanent storage in a database.     

基于模糊不确定性的自适应采样

提出一种用于MonteCarlo全局光照的自适应采样方法,使得合成图像时对每个像素采用不同的采样数量,以提高间接光照的表现效果,降低图像总体噪声水平．考虑到图像或像素噪声水平的评价具有内在的模糊不确定性,基于模糊理论,以像素样本光照为基本元素建立模糊集合,利用模糊集的模糊度提出一种新的像素噪声水平评价标准．在新评价标准的基础之上实施自适应采样,首先对像素进行少量采样,然后根据新标准评价其噪声水平,并有针对性地对噪声水平较高的像素使用较多的采样样本．通过大量实验,验证了文中方法比已有的自适应采样方法更好．     

Nature-inspired framework for measuring visual image resemblance: A near rough set approach

The problem considered in this paper is how to determine the degree of nearness between complex visual objects. The proposed solution to this problem stems from a natural computing approach to solving the visual acuity problem in terms of a granular representation of visual information that is quantifiable as well as understandable for humans. This is accomplished via a near rough set framework in the approximation of a pair of disjoint sets and measurement of distances between sets using various fuzzy pseudometrics. Pseudometrics, in general, and fuzzy pseudometrics, in particular, are useful in measuring the distance between pairs of objects such as sets. Such distances are indicators of the nearness of (resemblance between) visual objects. These observations lead to a number of practical applications such as object recognition and object retrieval in digital image analysis. One such application is reported in this article. The contribution of this article is threefold: introduction of a nature-inspired framework for measurement of visual object resemblance, four different incarnations of the standard fuzzy metric and application of fuzzy metrics in content-based image retrieval experiments.