基于频率、空间和方向的图像质量评估

将空间域图像质量评价方法结构相似度SSIM推广到HWD变换域,结合人眼视觉倾斜效应和粒子群优化算法,提出一种新的图像质量评价测度。将SSIM直接用于各HWD分解频带,用频带相关性图加权各频带的结构相似度得到局部质量,然后对不同方向的局部质量求加权和得到整幅图像的结构相似度。实验结果表明,该测度与主观感知有较好的一致性,能准确地反映人眼对图像的视觉感知。     

一种基于HWD结构相似的图像质量评价方法

针对传统图像质量评价方法峰值信噪比PSNR和结构相似度SSIM没有充分考虑人眼视觉特性,所得结果有时并不能与人眼的视觉所感知到的实际质量一致的问题,通过对图像结构相似度和人眼视觉系统的研究,文中提出了一种新的基于HWD结构相似的图像质量评价方法。首先对图像进行Hybrid Wavelets and Directional Filter Banks(HWD)分解,提取图像不同频带不同方向上的信息,然后计算各子带结构相似度,最后综合人眼视觉特性的CSF得到图像质量评价值。实验结果表明文中方法相比峰值信噪比PSNR、结构相似度SSIM算法具有更高的准确性和良好的相关性,可以更好的评价图像质量。     

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.     

基于参数估计的无参考型图像质量评价算法

针对现有客观图像质量评价算法通用性差及人类主观感知相关性弱等问题,该文提出一种基于参数估计的无参考型图像质量评价算法。该算法通过分析图像本身所蕴含的质量特征,提取出图像结构信息、颜色信息和视觉信息的评价度量指标,采用回归分析的方法估计出相应指标的参数。实验结果表明,该算法与主观评价结果一致性好、鲁棒性强,优于其它客观评价算法,能够准确反映人类对图像质量的视觉感知效果。     

基于视觉显著失真度的图像质量自适应评价方法

针对结构相似(SSIM)图像质量评价算法没有考虑人眼视觉多通道性和对图像高失真评价的不稳定性,提出一种基于视觉显著失真度的图像质量自适应融合(VSAP)评价方法。该方法首先采用log-Gabor滤波提取图像的高频、中频及低频3层视觉特征,基于log-Gabor变换尺度和方向权重系数计算特征值的相似度;然后基于视觉阈值多分辨性迭加计算出特征值的失真度;最后,根据视觉失真度自适应融合相似度评价与失真度评价获得图像质量的最终客观评价。实验结果表明,VSAP方法不但对图像不同类型失真的客观评价与主观感知具有更高的相关性,而且3个主要指标斯皮尔曼等级相关系数(SROCC)、曲线拟合相关系数(CC)和均方根误差(RMSE)对图像不同水平失真的整体评价性能更稳定,明显优于其它评价方法。     

基于人眼视觉的小波域结构相似性图像质量客观评价方法

针对空域结构相似性评价方法在几何失真图像及噪声污染图像的质量评价中存在的不足,在考虑人眼视觉特性前提下,研究了一种基于人眼视觉的小波域结构相似度的图像质量评价方法(WDSSIM),该方法首先将参考图像和失真图像进行小波变换,以获取不同尺度和频带的子带图像,并根据人眼视觉JND模型,获取人眼视觉对参考图像的带间敏感度和各子带的带内敏感度;然后,以带内敏感度因子为权值分别求取参考图像和失真图像同一尺度和频带的子带图像之间的结构相似度;最后,以带间敏感度因子为权值对各子带对的结构相似度进行加权归一化,获得整幅图像的结构相似度.     

基于人眼视觉特性的彩色图像质量评价

图像处理系统的性能优劣的评判往往需要一个合理迅速的图像质量评价算法作为支撑.传统的图像质量评价算法由于没有充分考虑人眼的视觉特性,使得质量评价结果与实际图像的人眼感知质量不符.根据人眼对图像边缘信息非常敏感这一人眼视觉特性,提出一种综合图像边缘和背录相似度的算法(EBS)来评价彩色图像质量,即通过比较失真彩色图像与原始参考图像的边缘以及除边缘之外的背景相似程度最终确定失真图像的质量.应用于由779幅包含五种类型失真的图像质量评价库的实验结果表明,该算法的评价结果相比PSNR,MSSIM,IFC以及基于像素域的VIF等算法与图像的主观评价结果(由DMOS值表示--将背景不同的一组观察者对失真图像的评分进行统计平均后所得到的评价结果)更一致,也即该算法的评价结果更接近图像的实际视觉感知质量.     

一种基于小波分析的部分参考型图像质量评价方法

部分参考型图像质量客观评价方法已经成为图像质量评价领域研究的热点之一。该文利用视觉感知特性,通过统计图像经小波分解后视觉感知系数在各子带中的变化情况,提出了一种基于小波分解的部分参考型图像质量评价方法。该方法与经典的RR-WISM(Reduced-Reference image quality assessment using Wavelet-domain natural Image Statistic Model)方法相比,主观感知的相关系数平均提高3%,主观感知的离出率平均降低6%,传输数据量减少50%,计算代价大大降低。实验结果表明提出的方法与主观感知有很好的一致性。     

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。     

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.     

Region adaptive subband image coding

We present a region adaptive subband image coding scheme using the statistical properties of image subbands for various subband decompositions. Motivated by analytical results obtained when the input signal to the subband decomposition is a unit step function, we analyze the energy packing properties toward the lower frequency subbands, edges, and the dependency of energy distribution on the orientation of the edges, in subband decomposed images. Based on these investigations and ideal analysis/synthesis filtering done in the frequency domain, the region adaptive subband image coding scheme extracts suitably shaped regions in each subband and then uses adaptive entropy-constrained quantizers for different regions under the assumption of a generalized Gaussian distribution for the image subbands. We also address the problem of determining an optimal subband decomposition among all possible decompositions. Experimental results show that visual degradations in the reconstructed image are negligible at a bit rate of 1.0 b/pel and reasonable quality images are obtainable at rates as low as 0.25 b/pel.     

Fractal coding of subbands with an oriented partition

We propose a new image compression scheme based on fractal coding of the coefficients of a wavelet transform, in order to take into account the self-similarity observed in each subband. The original image is first decomposed into subbands containing information in different spatial directions and at different scales, using an orthogonal wavelet-generated filter bank. Subbands are encoded using local iterated function systems (LIFS), with range and domain blocks presenting horizontal or vertical directionalities. Their sizes are defined according to the correlation lengths and resolution of each subband. The edge degradation and the blocking effects encountered at low bit-rates using conventional LIFS algorithm are reduced with this approach. The computation complexity is also greatly decreased by a 12:1 factor in comparison to fractal coding of the full resolution image. The proposed method is applied to standard test images. The comparison with other fractal coding approaches and with JPEG shows an important increase in terms of PPSNR/bit-rate. Especially for images presenting a privileged directionality, the use of adaptive partitions results in about 3 dB improvement in PPSNR. We also discuss the distorsion versus rate improvement obtained on high-frequency subbands when fractal coding instead of pyramidal vector quantization is used. Our approach achieves a real gain in PPSNR for low bit-rates between 0.3 and 1.2 bpp.     

基于二维APDCSF的列率子带特征编码方法

提出了一种子带编码的新方法。该方法利用二维全相位离散反余弦列率滤波器（APDCSF）对图像进行子带分解;对于低频子带图像采用直接斜交多重亚采样和基于全相位离散反余弦列牢滤波器（APDICsF）的多重旋转内插恢复．而对高频子带图像利用直方图自动阈值化提取如边缘和线等特征的图像元;根据各个子带的图像元的特征分别进行编码压缩,解压缩后利用凸集投影重建原始图像。该方法消除了传统的离散余弦变换（DCT）编码的方块化效应,与基于小波变换的子带特征编码方法相比,计算复杂度小,压缩率高,主观视觉性能好,对于灰阶图像可达到0．1～0．3bpp,特别适用于低比特率图像压缩。     

Adaptive MAP error concealment for dispersively packetized wavelet-coded images

In this paper, we present an adaptive maximum a posteriori (MAP) error concealment algorithm for dispersively packetized wavelet-coded images. We model the subbands of a wavelet-coded image as Markov random fields, and use the edge characteristics in a particular subband, and regularity properties of subband/wavelet samples across scales, to adapt the potential functions locally. The resulting adaptive MAP estimation gives PSNR advantages of up to 0.7 dB compared to the competing algorithms. The advantage is most evident near the edges, which helps improve the visual quality of the reconstructed images.     

Image identification and restoration in the subband domain

When faced with a large support point spread function (PSF), the iterative expectation-maximization (EM) algorithm, which is often used for PSF identification, is very sensitive to the initial PSF estimate. To deal with this problem, the authors propose to do EM image identification and restoration in the subband domain. After the image is first divided into subbands, the EM algorithm is applied to each subband separately. Since the PSF can be taken to have smaller support in each subband, these subbands should be less of a problem with the EM model identification. They also introduce an adaptive subband EM method for use in the upper frequency subbands.     

A subband-selective broadband GSC with cosine-modulated blocking matrix

A novel subband-selective generalized sidelobe canceller (GSC) for partially adaptive broadband beamforming is proposed. The columns of the blocking matrix are derived from a prototype vector by cosine modulation, and the broadside constraint is incorporated by imposing zeros on the prototype vector appropriately. These columns constitute a series of bandpass filters, which select signals with specific directions of arrival and frequencies. This results in a high-pass-type bandlimited spectra of the blocking matrix outputs, which is further exploited by subband decomposition and suitably discarding the low-pass subbands prior to running independent unconstrained adaptive filters in each nonredundant subband. By these steps, the computational complexity of a GSC implementation is greatly reduced compared to fully adaptive GSC schemes, while performance is comparable or even enhanced due to subband decorrelation in both spatial and temporal domains.     

A new time delay estimation in subbands for resolving multiplespecular reflections

A new time delay estimation procedure is proposed using the multiresolution analysis framework through a discrete wavelet transform (DWT). Once the signals are decomposed, the time delays are estimated iteratively in each subband using two different adaptation mechanisms that minimize the mean squared error (MSE) between the reference and primary signals in the corresponding subband and level. The localization of the minima of the MSE curves at different levels and subbands is used in order to arrive at the time delay estimates. The proposed scheme is then applied to a real-life problem of underwater target detection from acoustic backscattered data     

Tree coding of image subbands

The authors consider the encoding of image subbands with a tree code that is asymptotically optimal for Gaussian sources and the mean squared error (MSE) distortion measure. They first prove that optimal encoding of ideally filtered subbands of a Gaussian image source achieves the rate distortion bound for the MSE distortion measure. The optimal rate and distortion allocation among the subbands is a by-product of this proof. A bound is derived which shows that subband coding is closer than full-band coding to the rate distortion bound for a finite length sequence. The tree codes are then applied to encode the image subbands, both nonadaptively and adaptively. Since the tree codes are stochastic and the search of the code tree is selective, a relatively few reproduction symbols may have an associated squared error a hundred times larger than the target for the subband. Correcting these symbols through a postcoding procedure improves the signal-to-noise ratio and visual quality significantly, with a marginal increase in total rate.     

Invariant wideband spectrum sensing under unknown variances

In this paper, we divide a wide frequency range into multiple subbands and in each subband detect whether in a primary user (PU) is active or not. We assume that PU signal at each subband and the additive noise are white zeromean independent Gaussian random processes with unknown variances. We also assume that at least a minimum given number of subbands is vacant of PU signal and propose an invariant Generalized Likelihood Ratio (GLR) detector. The concept of the grouping of subbands allows faster spectrum sensing of a subset of subbands which may be occupied by a specific PU. Also, we evaluate trade-offs involved in the proposed algorithms by simulation.