三维网格客观质量评价

随着三维网格在数字游戏、影视动画、虚拟现实等领域的广泛应用,针对三维网格的处理方法也越来越多,包括压缩、简化、嵌入水印和去噪等,这些处理技术均不可避免地导致三维网格的失真。如何更好地评估三维网格的视觉质量成为目前亟需解决的问题。回顾了近20年来发展起来的三维网格的客观质量评价方法。介绍了这一领域常用评价方法的技术指标以及常用的数据库;对现有的典型三维网格的质量评价方法进行了分类并介绍了其各自的特点;在常用的数据库上对所介绍的典型算法进行性能测试和比较;对三维网格客观质量评价方法进行了总结与展望。     

Toward an unsupervised blind stereoscopic 3D image quality assessment using joint spatial and frequency representations

Existing blind stereoscopic 3D (S3D) image quality assessment (IQA) metrics usually require supervised learning methods to predict S3D image quality, which limits their applicability in practice. In this paper, we propose an unsupervised blind S3D IQA metric that utilizes the joint spatial and frequency representations of visual perception. The metric proposed in this work was inspired by the binocular visual mechanism; furthermore, it is unsupervised and does not require subject-rated samples for training. To be more specific, first, the various binocular quality-aware features in spatial and frequency domains are extracted from the monocular and cyclopean views of natural S3D image patches. Subsequently, these features are utilized to establish a pristine multivariate Gaussian (MVG) model to characterize natural S3D image regularities. Finally, with the learned MVG model, the final quality score for a distorted S3D image can be yielded using a Bhattacharyya-like distance. Our experimental results illustrate that, compared to related existing metrics, the devised metric achieves competitive prediction performance.     

Data Pre-Processing in Compressive Sensing Based Indoor Fingerprinting Positioning

Presented is a novel compressive sensing (CS) based indoor positioning approach, which uses the signal strength differentials (SSDs) as location fingerprints (LFs). By using certain kernel-based transformation basis, the 2-D target location is represented as an unknown sparse location vector in the discrete spatial domain. Then it just takes a little number of online noisy SSD measurements for the exact recovery of the sparse location vector by solving an ?₁-minimization program. In order to effectively apply CS theory for high precision indoor positioning, we further import some data pre-processing algorithms in the LF space. Firstly, to mitigate the influence of large measurements noise on the recovery accuracy, a LF space denosing algorithm is designed to discriminate the unequal localization contribution rate of every SSD measurement in each LF. The basic idea of the denosing algorithm is to transform the original LF space into a robust and decorrelated LF space. Moreover, in order to lower the high computational complexity of the CS recovery algorithm, several LF space filtering algorithms are also exploited to remove certain percentage of useless LFs in the radio map according to the real-time RSS observations. The performance of these denosing and filtering algorithms are investigated and compared in real-world WLAN experiment test. Both experimental results and simulations demonstrate that we achieve remarkable improvements on the positioning performance of the CS based localization by using the proposed algorithms.     

基于人眼视觉特性的深度学习全参考图像质量评价方法

针对现有的图像质量评价方法普遍为人工设计特征,难以自动且有效提取到符合人类视觉系统的图像特征,受人眼视觉特性的启发,提出一种新的基于卷积神经网络的全参考图像质量评价方法（DeepFR）。该方法基于对数据集本身的学习设计了卷积神经网络DeepFR模型,利用人眼视觉系统对梯度的敏感性进行加权优化,提取了符合人眼视觉特性的视觉感知图。实验表明:设计的DeepFR模型优于已有的全参考图像质量评价方法,其预测结果与主观质量评价有很好的精确性与一致性。     

离散小波变换的最优Lifting分解方法

现有Lifting分解(Lifting Factorization,LF)算法不能在较短时间内求解出较长离散小波变换(Discrete Wavelet Transform,DWT)滤波器组的所有数值稳定LF,更无法计算最优LF.在LF的形式化分析基础上,提出了DWT多相矩阵的常数最大公因子分解方法,极大缩小了分解搜索空间;进一步,从数值稳定性及计算复杂性两方面研究了LF评价问题;最后,提出了基于联合优化的最优LF算法.实验结果表明,本文算法在解的质量、求解时间及适用范围上都有较大改进.     

基于三维结构张量的立体图像质量客观评价方法

根据梯度结构张量能够表示图像结构信息的特点, 提出了一种基于三维结构张 量的立体图像客观质量评价方法。首先分别求取原始和失真的立体图像水平、垂直和 视点方向的梯度信息,以及敏感区域,并构造出立体图像中每个像素的三维结构张量矩 阵;然后,提 取三维结构张量矩阵的特征值和特征向量信息;最后,根据特征值和特征向量预测得到立体 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图像测试库进行评价,总体 评价的Pearson 线性相关系数(PLCC)和Spearman等级相关系数(SROCC)值均在0.92左右,Kendall相关系数 (KROCC)值 接近0.80,均方根误差(RMSE)值均在6.00左右;与其他方法相 比,本方法具有较高的预测精确性。     

视频通信业务质量客观评价方法综述

给出了多媒体通信中视频客观质量评价方法的综述及其在ITU中的标准制定进程.首先介绍了视频质量客观评价模型的分类,之后分别针对网络规划和网络监控的应用介绍了各类模型的应用和发展状况,最后展望了用于网络监控的质量评价模型未来的发展方向.     

一种基于用户关注模型的立体图像舒适度客观评价方法

近年来,立体电视和立体电影的发展掀起了一股“3D”热潮。要让观众享受到高质量的立体内容,首先要解决人们在观看立体视频图像时的不舒适感。本文以立体图像的视差因素为切入点,结合人眼关注模型,设计了并实现了一种立体图像舒适度的客观评价方法。该方法根据人造立体图像的主观实验评价结果,建立了视差——舒适度模型,同时结合图像显著信息,对立体图像的舒适度进行了预测。对比主观评价结果,本文实现的所提出的立体图像舒适度客观评价方法所获取的评价结果与人眼感受具有较好的一致性。     

基于视觉重要区域的立体图像视觉舒适度客观评价方法

先进的立体视频技术能给观众带来深度感和沉浸感,但也容易使人产生视觉疲劳,造成观看体验质量的下降,因此,如何对立体视频/图像的视觉舒适度进行有效评价是目前的研究难点。该文提出一种基于视觉重要区域的立体图像视觉舒适度客观评价模型,该模型主要包括3个部分:(1)利用图像显著图和视差图像得到立体图像视觉重要区域;(2)提取视觉重要区域的视差幅度特征、视差梯度边缘特征以及空间频率特征,作为反映立体图像视觉舒适度的感知特征信息;(3)通过支持向量回归建立特征信息与立体图像舒适度平均主观评分值的关系,预测得到立体图像视觉舒适度的客观评价值。实验结果表明,与现有的方法相比较,在相同的立体图像测试库上,采用该文提出的客观评价模型可以获得更好的评价性能。     

No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.     

No-reference Stereoscopic Image Quality Assessment Using Binocular Self-similarity and Deep Neural Network

Quality assessment of three-dimensional (3D) images is more challenging than that of 2D images. The quality of 3D visual experience is one of the most challenging areas of human binocular perception and is affected by multiple factors such as asymmetric stereo image/video compression, depth perception, visual discomfort, and single view quality. In this paper, we propose a new no-reference quality assessment method for stereoscopic images based on Binocular Self-similarity (BS) and Deep Neural Networks (DNN). To be more specific, a BS index is defined and computed according to binocular rivalry and suppression based on the depth image-based rendering technique. Then, a DNN is trained in an opinion unaware way to predict local quality. Binocular integration (BI) index is calculated by using the trained DNN, accounting for binocular integration behaviors. Finally, the final quality score of stereoscopic image is obtained by combining the BS and BI indexes together. Experimental results on four public 3D image quality assessment databases demonstrate that compared with existing methods, the proposed method can achieve high consistency with subjective perception on stereoscopic images with both symmetric and asymmetric distortions.     

基于多特征融合的三维网格质量评价方法

三维网格客观质量方法研究对三维网格处理方法设计有重要指导意义。现有的三维网格质量评价方法大多采用单一特征来表征三维网格的失真程度,难以全面描述人眼对三维网格失真的视觉感知。在提出基于分区的曲率奇异值分解的结构相似性(CSSIM)特征的基础上,进一步提出多特征融合的三维网格质量评价方法。提取的特征通过随机森林的聚合,训练得到三维网格的客观质量评价模型,预测评价分数。经过在两个公开的三维网格数据库(LIRIS/EPFL general-purpose和LIRIS Masking)上的验证,其结果表明相比现有代表性方法,所提出方法的评价结果与人眼主观感知评价结果的一致性更好,表面其符合人眼的视觉感知。     

Objective and subjective evaluation of High Dynamic Range video compression

A number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.     

利用边缘相似度的光场图像质量评价

近年来，随着虚拟现实（Virtual Reality，VR）和增强现实（Augment Reality，AR）技术发展和普及，光场图像引起了学术界和工业界的广泛关注。然而，在光场采集和图像压缩、存储、传输和渲染的过程中，不可避免会引入各类失真从而导致光场图像质量出现劣化。因此，如何根据人眼视觉特性来准确高效地评价光场图像质量成为急需解决的问题。考虑到光场图像复杂的结构特性，本文意在利用边缘相似度来构建适用于光场图像质量客观评估的数学模型。首先, 利用梯度和Gabor滤波器分别提取光场图像的空域和频域相似度，进而进行融合得到边缘相似度图，接着对边缘相似度图采用基于频域边缘强度的池化策略进行权重计算得到最终的客观评估分数。实验结果显示，与现有的图像质量评价方法相比，本文所提算法能够更好地反映出人类视觉系统对光场图像的主观感知特性。      

音频质量客观评价方法的综合与实现

以ITU—RBS．1387—1为基础，讨论了多种音频质量客观评价的改进方法。在此基础上提出了“对各种改进方法加以综合，可进一步提高相关度系数”的假设。结合国、内外相关文献，进行了试验性地优化，最终实现了可视化仿真程序OE（Objective Evaluation）设计，主、客观评价相关度达到了0．85以上。     

无参考视频通信业务质量客观评价方法综述

近年来,多媒体通信业务飞速发展,对多媒体通信的传输和接收质量进行实时评价和监测有助于端到端业务质量的管理和控制。对视频通信业务质量的评价可通过主观和客观两个方面展开,主要介绍无参考视频质量客观评价方法。首先介绍视频通信中常见的损伤类型,然后分别详细阐述基于像素级、比特流级和混合参数级三种模型的研究现状及相应的标准化进程。     

交互式视频质量评价方法研究进展

在交互式视频应用快速发展的同时,如何评价视频质量成为当前亟待解决的挑战性难题,其成果对整个多媒体通信系统的各环节技术发展具有关键作用。从主观质量评价、客观质量评价两个角度综述了当前交互式视频质量评价的研究与应用现状,其中主观质量评价方法包括主观视频质量评价数据库、主观视频质量评价打分与计算机制,客观质量评价方法则包括视觉信号处理与分析、深度学习机制下的评价与建模方法等。在总结上述研究方法与成果的基础上,展望了本领域的研究与发展。     

深度视觉频谱残余融合的图像质量评价

针对现有图像质量评价方法的缺陷,通过深度学习理论建模人眼视觉系统(human vision system,HVS)特性,提出了一种基于视觉特征深度感知与学习融合(deeply perception and learning for pooling,DPLP)的评价方法.首先为了增加图像视觉特征的稳定性,根据人眼感光的空域结构特征和频域多通道特性,对图像依次进行二维Log-Gabor小波变换、梯度变换和频谱残余的深度视觉信息处理,然后分别提取各层视觉信息进行质量评价.其次为了克服HVS融合的不确定性,对质量评价信息采取了深度池化策略,第一层为评价视图的空域融合,采取了符合人眼感光特性的高斯加权策略;第二层为多通道评价的频域融合,采取了具有HVS推理能力的BP神经网络的学习-预测策略;第三层为各级视觉特征的评价融合,采取了具有自适应特性的回归函数策略.最后,基于现实中的各种失真类型图像进行了实验,结果表明所提方法具有较高的主客观评价一致性水平和更好的稳定性.