浅谈谱质心对弹拨乐单音悦耳度的影响

探讨了民族弹拨乐器声学特性的客观参数中,衡量能量分布的谱质心参数与主观听觉悦耳度之间的联系.挑选了一些常见的民族弹拨乐器的单音进行音质主观评价,利用多元统计分析方法将整理的主观评价数据与谱质心的客观参数进行拟合,较全面地了解谱质心对民族弹拨乐单音悦耳度的影响.对民族乐器客观分析研究做了初期探究基础,为专家学者广泛研究民族乐器声学领域提供参考.     

基于MVDR谱的语音通信干扰效果评估方法研究

提出了基于MVDR谱的语音通信干扰效果客观评估方法,并在通信电台连续波辐照试验中进行了验证。试验结果表明:该方法较好地反映了人耳听觉特性,与主观音质评价相一致,是一种较好的可以应用于语音通信干扰效果评估的客观方法。     

一种基于非均匀谱系数和GMM的语音质量评估方法

本文提出了一种新的基于GMM和非均匀线性预测倒谱系数(NLPC)的客观语,音质量评估方法.首先,通过Bark双线性变换(BBT)对线性频谱进行频谱弯折,弯折后的频谱符合人耳听觉感知的非均匀特性.然后通过对非均匀谱的线性预测计算出NLPC.提取参考语音的NLPC用来对高斯混合模型进行训练.通过训练对参考语音建立参考模型.由参考模型和失真语音的NLPC向量可以得到它们之间的一致性测度.最后,通过多元自适应回归样条函数建立主观MOS分和一致性测度之间的映射关系,可以得到对MOS分的客观预测模型.通过这一模型进行语音质量的客观评价.实验表明,提出算法的性能要好于ITU-T P.563标准中的算法.     

基于小波变换的语音质量客观评估方法研究

对小波变换原理进行简要分析的基础上,对小波变换与小波美倒谱(MFCC)方法相结合进行语音客观音质评价的方法进行了研究,给出了小波美倒谱语音音质评估原理及计算流程。使用MFCC方法和小波美倒谱算法分别计算原始语音文件与受扰语音文件的失真距离,并将失真距离与主观评测结果进行相关分析,得出相应算法的相关系数和方差值,通过对比表明小波美倒谱语音评估方法对于以倒谱域参数为基础的客观评价方法有很大改善。     

计权失真度和它的测量

本文叙述总谐波失真和主观失真的关系，主观失真相应于实际听觉感觉，求得了一组人耳对各次谐波失真的阈值，然后设计了有专用计权网络的电子设备，可以按照这样曲线测量电声系统的失真。     

用于语音干扰效果评估的加权对数谱分析法

语音通信质量客观评估方法是当前声学技术研究领域的一个重要课题。在介绍语音音质评估一般方法的基础上,对基于巴克谱的客观评估结果进行分析对比后发现,评估结果中失真距离与主观评估的一致性较差,提出了基于加权对数谱分析的语音干扰效果客观评估方法,使评估结果更加可靠。     

一种基于最小可察觉失真的立体图像质量客观评价方法

立体图像质量是评价立体视频系统性能的有效途径,而如何利用人类视觉特性对立体图像质量进行有效的评价是目前的研究难点。本文通过分析最小可察觉失真(JND,just noticeable distortion)视觉感知模型,并结合反映图像结构信息的奇异值矢量,提出了一种基于JND的立体图像质量客观评价方法。评价方法由图像质量评价和深度感知评价两部分组成,首先提取反映图像质量和深度感知的特征信息作为立体图像特征信息,然后根据立体图像的不同失真类型情况对其特征进行融合,通过支持向量回归(SVR,support vector Regression)预测得出立体图像质量的客观评价值。实验结果表明,采用本文提出的客观评价方法对立体数据测试库进行评价,在不同失真类型或混合失真评价结果中,Pearson线性相关系数(CC)值均在0.94以上,Spearman等级相关系数(SROCC)值均在0.92以上,符合人眼视觉特性,能够很好地预测人眼对立体图像的主观感知。     

微型扬声器谐波失真的分析与调制

以谐和性听觉感知研究为基础,在不改变微型扬声器结构的前提下,对微型扬声器的客观参数进行测量,并对微型扬声器重放音质的谐和性进行听音评价实验.结合主客观的相关分析,对输入微型扬声器的信号进行谐和性调制,以弥补微型扬声器谐波失真的缺陷,提高微型扬声器重放信号的谐和感.     

一种基于人耳听觉特性的语音客观测度研究

文章研究基于人耳听觉特性的Ｂａｒｋ谱测度评价方法。由若干２测试语音条件的客观失真距离和偏差的计算结果表明,该测度具有较高的主、客观相关系数,并在此基础上,与ＬＰＣ－ＣＤ测度进行了比较。     

基于E1接口感知语音质量测试设备的实现方案

语音音质评价从主体上可分为主观评价和客观评价两种。由于音质好坏最终取决于人的主观感受,所以在语音系统中主要采用主观评价的方法。但是这种方法费时费力,同时受到测试条件和测试人员主观因素的影响,降低了测试结果的可靠性。针对上述缺点,设计了一种客观评价语音音质的测试设备实现方案,该测试设备基于E1接口,采用ITU-T P.862 PESQ算法模型。     

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。     

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.     

An objective measure for predicting subjective quality of speechcoders

A perceptually motivated objective measure for evaluating speech quality is presented. The measure, computed from the original and coded versions of an utterance, exhibits statistically a monotonic relationship with the mean opinion score, a widely used criterion for speech coder assessment. For each 10-ms segment of an utterance, a weighted spectral vector is computed via 15 critical band filters for telephone bandwidth speech. The overall distortion, called Bark spectral distortion (BSD), is the average squared Euclidean distance between spectral vectors of the original and coded utterances. The BSD takes into account auditory frequency warping, critical band integration, amplitude sensitivity variations with frequency, and subjective loudness     

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.     

客观评估彩色图像质量的超复数奇异值分解法

本文利用超复数直接对彩色图像建模,保存了彩色图像完整的信息;基于超复数奇异值分解(也称四元数奇异值分解QSVD)提出一种全新的图形化与数值化相结合的彩色图像质量评估测度,不仅能判断图像失真等级,还能判断不同的失真类型.测试结果表明,本文提出的算法比传统的MSE、PSNR以及MSSIM等算法性能更优.     

No-reference image quality assessment based on spatial and spectral entropies

We develop an efficient general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features on distorted images. Using a 2-stage framework of distortion classification followed by quality assessment, we utilize a support vector machine (SVM) to train an image distortion and quality prediction engine. The resulting algorithm, dubbed Spatial–Spectral Entropy-based Quality (SSEQ) index, is capable of assessing the quality of a distorted image across multiple distortion categories. We explain the entropy features used and their relevance to perception and thoroughly evaluate the algorithm on the LIVE IQA database. We find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top-performing NR IQA methods: BIQI, DIIVINE, and BLIINDS-II. SSEQ has a considerably low complexity. We also tested SSEQ on the TID2008 database to ascertain whether it has performance that is database independent.     

视频图像中端至端失真估算的研究

视频图像数据量大、压缩码流抗误性低,而现有通信网络的承载能力有限、传输性能不可靠,往往造成解码重建视频图像质量致命性地降低。有效控制信源与信道编码,使端至端失真最小是提高解码重建视频图像质量的关键,而精确的失真估算模型是获得最小端至端失真的前提。对几种典型的端至端失真估算模型进行了简要分析,指出了这些模型中待解决的关键问题并展望了端至端失真估算模型良好的发展前景。     

An SVD-based grayscale image quality measure for local and global assessment.

The important criteria used in subjective evaluation of distorted images include the amount of distortion, the type of distortion, and the distribution of error. An ideal image quality measure should, therefore, be able to mimic the human observer. We present a new grayscale image quality measure that can be used as a graphical or a scalar measure to predict the distortion introduced by a wide range of noise sources. Based on singular value decomposition, it reliably measures the distortion not only within a distortion type at different distortion levels, but also across different distortion types. The measure was applied to five test images (airplane, boat, goldhill, Lena, and peppers) using six types of distortion (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening, and DC-shifting), each with five distortion levels. Its performance is compared with PSNR and two recent measures.     

IQM2: new image quality measure based on steerable pyramid wavelet transform and structural similarity index

In this paper, we present a new full-reference objective image quality measure—IQM2, based on structural similarity index and steerable pyramid wavelet transform. IQM2 is tested using different number of orientation kernels and seven subjective databases. Finally, IQM2 measure is compared with twelve commonly used full-reference objective measures. Results show that proposed IQM2 measure, using kernel with 2 orientations, provides good correlation with the results of subjective evaluation while keeping computational time lower than other similar performing objective measures.     

MIQM: A Multicamera Image Quality Measure

Although several subjective and objective quality assessment methods have been proposed in the literature for images and videos from single cameras, no comparable effort has been devoted to the quality assessment of multicamera images. With the increasing popularity of multiview applications, quality assessment of multicamera images and videos is becoming fundamental to the development of these applications. Image quality is affected by several factors, such as camera configuration, number of cameras, and the calibration process. In order to develop an objective metric specifically designed for multicamera systems, we identified and quantified two types of visual distortions in multicamera images: photometric distortions and geometric distortions. The relative distortion between individual camera scenes is a major factor in determining the overall perceived quality. In this paper, we show that such distortions can be translated into luminance, contrast, spatial motion, and edge-based structure components. We propose three different indices that can quantify these components. We provide examples to demonstrate the correlation among these components and the corresponding indices. Then, we combine these indices into one multicamera image quality measure (MIQM). Results and comparisons with other measures, such as peak signal-to noise ratio, mean structural similarity, and visual information fidelity show that MIQM outperforms other measures in capturing the perceptual fidelity of multicamera images. Finally, we verify the results against subjective evaluation.