符合人眼视觉特性的视频质量评价模型

视频技术的发展为其质量评价的出了新的课题,但由于评价图像质量的关键在于所用视觉模型是否符合人的感知特性,因此评价图象质量必须考虑以视觉锐度,对比度敏感度,多通道结构和掩盖特性为基础的人眼视觉特性（HVS）,为了使人们对基于人眼视觉特性的视频质量评价模型研究现状有所了解,介绍了几种目前比较成功的基于HVS的视频质量评价模型,并分析和总结了它们的性能,最后展望了评价模型的发展。     

显著度和运动特性加权的视频质量评价方法

进行客观视频质量评价时,为了与主观评价结果尽可能一致,需要考虑视频的动态特性和人眼观看的视觉特性,因此本文提出一种基于显著区域和运动特性加权的视频质量评价方法。该评价指标基于传统的结构相似性指数(Structural similarity index measurement, SSIM)方法并在此基础上作了改进。首先通过频谱分析得到空域显著度,通过视觉注意模型并结合运动特性获取时域显著度,并根据时、空显著度动态融合得到帧级显著度。以帧级显著度加权SSIM指数,便可得到整个视频帧的质量评价指标。在LIVE VQA标准数据集上的实验结果表明,该评价指标更加接近于人眼对视频质量的主观评价值。     

基于时域梯度相似度的视频质量评价模型

频质量评价有助于多媒体网络系统优化和视频编解码算法改进,近年来已成为图像质量评价领域的热门研究方向。在图像质量评价模型(FSIM)的基础上,结合视频局部多帧之间的时域相关性,通过采用新型的三维梯度算子计算原始视频序列与失真视频序列间的梯度相似度矩阵,提出了一种基于时域梯度相似度的视频质量评价模型(TGSM FSIM)。在LIVE视频数据上的测试结果表明,所提模型与视频主观评价有较好的一致性,SROCC与PLCC指标优于VSSIM和VQM两种广泛使用的视频质量评价算法。     

基于H.264码流的无参考视频质量评价模型

对视频进行质量评价时,为了不对视频序列完全解码,论文提出了一种基于H.264码流的无参考视频评价模型。该模型只需从码流中解析视频的量化参数、编码比特率和"跳跃"块等信息。论文首先确定了量化参数和主观质量之间的关系,然后分析视频的时间复杂度和空间复杂度,最终利用人眼的空间掩盖效应和时间掩盖效应建立一个能反映人眼视觉特性的视频质量评估模型。实验表明该模型计算出的视频序列的质量和视频的主观质量评价有很好的相关性,能用于对H.264视频进行质量评价。     

基于深度学习的视频质量评价研究综述

视频质量评价(VQA)是以人眼的主观质量评估结果为依据,使用算法模型对失真视频进行评估.传统的评估方法难以做到主观评价结果与客观评价结果相一致.基于深度学习的视频质量评价方法无需加入手工特征,通过模型自主学习即可进行评估,对视频质量的监控和评价有重要意义,已成为计算机视觉领域的研究热点之一.首先对视频质量评价的研究背景...     

适用小样本的无参考水下视频质量评价方法

目的 视频质量评价是视频技术研究的关键之一。水下环境比其他自然环境更加复杂,自然光在深水中被完全吸收,拍摄所用的人工光源在水中传播时会发生光吸收、色散和散射等情况,同时受水体浑浊度和拍摄设备等影响,导致水下视频具有高度的空间弱可视性和时间不稳定性,常规视频质量评价方法无法对水下视频进行准确、有效的评价。本文考虑水下视频特性,提出一种适用小样本的结合空域统计特性与编码的水下视频质量评价方法。方法 基于水下视频成像特性,建立新的水下视频数据库,设计主观质量评价方法对所有视频进行15分质量标注。从水下视频中提取视频帧图像,针对空间域计算图像失真统计特性,然后结合视频编码参数,通过训练线性模型权重系数完成水下视频的质量评价。结果 实验表明,与几种主流的质量评价方法相比,本文水下视频质量评价方法与人类视觉感知的相关性最高,模型评价结果与主观质量评价结果的皮尔森线性相关系数PCC（Pearson''s correlation coefficient）为0.840 8,斯皮尔曼等级秩序相关系数SROCC（Spearman''s rank order correlation coefficient）为0.832 2。通过比较各方法评价结果与真实值的均方误差（mean square error,MSE）,本文方法MSE值最小,为0.113 1,说明本文的质量评价结果更加稳定。结论 本文通过空间域单帧图像自然场景统计特性和视频编码参数融合的方式,提出的无参考水下视频质量评价方法,能够很好地运用小样本水下视频数据集建立与人类视觉感知高度相关的评价模型,为水下视频做出更准确的质量评价。     

一种结合感知与融合的视频质量评价新方法

提出了一种结合人眼视觉特性(HVS)和信息融合的视频质量评价新方法.该方法是在结构相似(SSIM)方法基础之上,融合了人眼几个主要视觉特性,如对比敏感度、多通道、视觉掩盖、视觉非线性等.新方法具有SSIM算法简单、高效等特性,同时又满足人眼视觉特性,更好地反映了人的主观感受.通过VQEG Phase I测试数据集的实验结果证明,该方法在非线性回归后相关系数、斯皮尔曼相关系数、线外率等指标均优于传统的其他视频质量评价算法,有效地提高了视频质量评价的主客观一致性.     

变权无参考视频质量评价模型

由于视频图像在传输过程中信道噪声将导致质量下降,在无需增加传输信息的前提下客观无参评价方法可实现视频质量的自动评估,因此成为一个重要研究课题。为了提高无参考评价方法准确性,提出了符合人类视觉特性的变权评价模型,综合考虑空域中的清晰度和时域中的平滑度两类指标,利用视频内容的运动信息控制权重变化,模型评价结果与主观评价符合度高,简单相关系数为0.85。实验结果表明,符合视觉特性的连续变权方法比固定权值模型准确,计算复杂度比同类研究方案小,具有更大的应用价值。     

变权无参考视频质量评价模型研究

传输信道失真是导致视频图像质量损失的重要原因,而不需要任何附加传输信息的无参考客观评价是监测视频传输质量受损的主要方法.为了提高无参考客观评价模型的准确性和效率,提出模拟人类视觉特性的变权评价模型.变权评价模型综合考虑视频的空域和时域两类质量指标,引入运动强度来量化视频内容中的运动变化程度,根据统计学习的非线性回归法建立变权控制函数;通过变权控制函数动态调整清晰度和平滑度权值来模仿人类视觉特性.实验结果表明该变权评价模型与主观评价符合度高,优于现有评价方案.     

FGS视频流的码率分配算法研究

该文针对精细可分级编码(FGS)比特流在时变带宽网络上的传输,提出了一种基于视频序列率失真(R-D)特性的FGS增强层的码率分配算法,目标是减少接收端解码视频质量的波动,同时保持视频总体质量最优。首先建立一个在多帧图像增强层之间进行码率分配的最优化问题形式,并进行了合理的简化,然后利用线性内插原则建立描述各帧图像增强层率失真特性的R-D模型。由于各帧图像R-D曲线的单调特性,如此建立起来的最优化问题可以用简单的算法求出最优解。仿真结果表明,这个方案在保证解码视频质量恒定和保持视频总体质量最优两方面均收到了良好的效果,同时该方案的简易性使得它的实现和应用成为可能。     

Error sensitivity model based on spatial and temporal features

Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.

     

三网融合下基于压缩感知的半参考视频质量评估方法研究

针对三网融合条件下的视频质量模型、评估体系和设计方案, 提出了一种基于压缩感知的半参考视频质量评估方法。该算法首先建立视频质量模型, 并通过压缩感知算法获取视频图像的感知系数; 然后利用视频图像的稀疏边缘分布去表征不同图像失真模型的相关性; 最后利用一个信息距离测度测量这些概率分布并进行量化。在标准测试图像库上的实验验证了该算法的有效性和对于不同失真模型的适应性, 特别是更适合于运营商在多屏、多制式、多终端、多分工界面等特点的三网融合复杂环境下的应用。     

Automatic prediction of perceptual quality of multimedia signals—a survey

We survey recent developments in multimedia signal quality assessment, including image, audio, video, and combined signals. Such an overview is timely given the recent explosion in all-digital sensory entertainment and communication devices pervading the consumer space. Owing to the sensory nature of these signals, perceptual models lie at the heart of multimedia signal quality assessment algorithms. We survey these models and recent competitive algorithms and discuss comparison studies that others have conducted. In this context we also describe existing signal quality assessment databases. We envision that the reader will gain a firmer understanding of the broad topic of multimedia quality assessment, of the various sub-disciplines corresponding to different signal types, how these signals types co-relate in producing an overall user experience, and what directions of research remain to be pursued.     

视频质量客观评估方法分析

文章简单介绍了视频监控系统的应用,指出了视频质量主观评估的缺点,并按不同的分类分析了常用的视频质量客观评估方法,在此基础上探索了一种视频质量无参评客观估方法的思路。     

Perceptual quality assessment of nighttime video

The quality of nighttime videos is important for consumer photography and monitoring of video clearness. However, little work has been done on the study of nighttime video quality assessment. In this paper, to the best of our knowledge, we explore the study on the nighttime video quality assessment for the first time. First, we build a real-world nighttime video quality assessment database (NVQA) containing 200 videos with abundant content and diverse distortion. Additionally, we carry out subjective tests to rate all nighttime videos in the NVQA database. Thereafter, we proposed a blind nighttime video quality assessment model based on feature fusion and conducted experiments to evaluate the performance and efficiency of our proposed model. The experiment results demonstrate that our model outperforms most traditional methods.     

Modeling RPS and Evaluating Video Repair With VQM

Reference picture selection (RPS) is a promising repair technique in lossy networks for delay-sensitive video, whereby the video encoder uses one of several previous frames as a reference frame for predictive encoding. RPS can operate in two different modes: an optimistic policy that uses negative acknowledgements (NACKs) and a more conservative policy that relies upon positive acknowledgements (ACKs). This paper compares RPS NACK and RPS ACK under various network conditions and video contents using two analytical models. The two models characterize RPS NACK and RPS ACK by incorporating the impact of reference distance on video quality, prediction dependency among video frames and Group of Pictures (GOP) length. Given packet-loss rate, round-trip time and capacity constraints, the models predict average video quality for videos with RPS ACK and RPS NACK using the Video Quality Metric (VQM). Using these two models, a series of experiments are conducted to investigate RPS performance under various conditions. The insights derived from our models can help determine appropriate choices for RPS NACK and RPS ACK under various network conditions and video contents.     

基于线性回归分析的部分参考视频质量评估方法*

提出了一种基于线性回归分析的部分参考视频序列质量评估算法。该算法主要以原始视频的帧间差异作为参考信息,通过分析编码前后视频时域活动性的变化和单帧比特数的变化关系来客观评价视频序列的编码质量。该方法只需少量原始参考信息,算法简单。通过对标准视频序列的仿真实验,该算法可有效评价不同视频的编码质量,使用该质量评估方法测得的失真视频客观质量评分与其主观质量评分有较好的一致性。     

Quality assessment of perceptual color video based on a top-down framework and quaternion

Objective video quality assessment is of great importance in a variety of video processing applications. Most existing video quality metrics either focus primarily on capturing spatial artifacts in the video signal, or are designed to assess only grayscale video thereby ignoring important chrominance information. In this paper, on the basis of the top-down visual analysis of cognitive understanding and video features, we propose and develop a novel full-reference perceptual video assessment technique that accepts visual information inputs in the form of a quaternion consisting of contour, color and temporal information. Because of the more important role of chrominance information in the “border-to-surface” mechanism at early stages of cognitive visual processing, our new metric takes into account the chrominance information rather than the luminance information utilized in conventional video quality assessment. Our perceptual quaternion model employs singular value decomposition (SVD) and utilizes the human visual psychological features for SVD block weighting to better reflect perceptual focus and interest. Our major contributions include: a new perceptual quaternion that takes chrominance as one spatial feature, and temporal information to model motion or changes across adjacent frames; a three-level video quality measure to reflect visual psychology; and the two weighting methods based on entropy and frame correlation. Our experimental validation on the video quality experts’ group (VQEG) Phase I FR-TV test dataset demonstrated that our new assessment metric outperforms PSNR, SSIM, PVQM (P8) and has high correlation with perceived video quality.     

Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change

Due to the variability of wireless channel state, video quality monitoring became very important for guaranteeing users’ Quality of Experience (QoE). QoE presents the overall perceptual quality of service from the subjective users’ perspective. However, because of diverse characteristics of video content, Human Visual System (HVS) cannot give the same attention to whole scene simultaneously when facing video sequence. In this paper, we proposed a video quality assessment model by considering the influence of fast motion and scene change. The motion change contribution factor and scene change contribution factor are defined to quantify the characteristics of video content, which is closely related to the users’ QoE. Based on G.1070, our proposed model considers the influential factors of loss nature of video coding, variability of practical network and video features. Also, the proposed model owns low computational complexity due to the compressed domain approach for the estimation of the model parameters. Therefore, the video quality is assessed without fully decoding the video stream. The performance of our proposed model has been compared with five existing models and the results also shown that our model has high prediction accuracy closing to human perception.