4K超高清播出系统设计研究

作为我国未来电视产业发展战略的重要建设内容,4K超高清播出系统在提升电视节目播出质量和效果、提升电视媒体影响力和体验感等方面具有积极的作用。因此,结合广播电视台相关实践现状,在概括介绍4K超高清播出系统的基础上,技术性地分析探讨超高清系统的设计思路与设计时需要注意的问题,从而为广播电视台建设优质的4K超高清播出系统提供参考。     

中央广播电视总台2021年春节联欢晚会技术创新及应用

中央广播电视总台依托超高清视音频制播呈现国家重点实验室全面推进5G、4K/8K、AI、AR/VR等新技术的研发,在2021年春节联欢晚会实现了8K超高清试验频道全球首次8K直播、4K春晚上海传媒港影院同播、AI+VR裸眼3D拍摄制作及超高清VR+沉浸式三维声直播等全新应用模式,这些技术创新为电视观众和新媒体用户带来了焕然一新的视听体验。本文对整体技术创新内容进行了介绍和总结。     

5G+4K超高清视频产业发展前景分析

超高清是指在数字化高清技术之后的一项重大技术突破.5G+4K相互协调在一定程度加快了依照超高清视频技术产业逐渐向智能化的发展速度.通过5G+4K超高清技术,有线、广播电视迎来的全新的升级,提升了用户的体验.文章对5G+4K的技术背景、现阶段的发展状况进行了详细的分析,从超高清5G视频的的采集、制作、传输及城乡等各个业务环节以及购物、医疗等人们生活的方方面面,对5G技术对4K超高清技术产实业的支撑及促进进行了阐述,意在为5G+4K超高视频产业的稳定发展提供参考与借鉴.     

三网融合背景下4K超高清智能用户QoE感知系统应用研究

本文通过基于智能电视终端视频质量监测系统描述了4K超高清智能用户视频QoE感知的技术原理、技术实现及详细架构流程设计。针对4K电视业务的网络环境,给出了4K超高清智能用户QoE感知系统的改进方案,使4K新型视频业务能够实现高效精准的用户质量保障。     

4K技术在我国影视行业中的应用与发展

4K技术极大地提升了视频清晰度,给用户带来了极致的沉浸式观影体验和视觉享受,满足了受众对影视节目制作质量不断提升的要求,因此被广泛应用于影视行业。从诞生至今,4K超高清技术已经逐步完善,目前已经向8K技术进发。梳理4K技术在我国影视行业的应用和发展历程,可以更好地促进4K技术的进一步发展,同时为8K技术的普及和发展提供参考。     

考虑时间-空间特性的流媒体视频QoE评价模型

流媒体视频服务作为目前网络服务最广泛的应用之一,其用户感知质量已经引起了服务提供商的广泛关注。文章提出了一种预测流媒体视频用户体验质量（QoE）的无参考评价模型。该模型基于人类视觉系统（HVS）,考虑视频时间-空间特性,讨论了编码压缩、网络丢包因素对视频质量的影响情况,与用户感知评分（MOS）相关度达到0.965,且二者均方根误差小于0.298,能够较为准确地预测流媒体视频用户感知质量。     

基于双眼视觉感知特征一致性的立体图像体验质量评价

3D图像被认为是多媒体技术的重要标志,其中,立体图像质量对3D图像发展起到至关重要的作用。不同于传统的2D图像质量评价,在3D图像质量评价中引入关于体验质量（ QoE）问题的新挑战,因此,本文提出一个基于双眼视觉感知特征一致性的立体图像体验质量评价算法。具体地,先对2个视点图像提取像素梯度作为视觉感知的低层次特征,再用梯度方向直方图特征（ HOG）建立立体图像的视觉感知特征向量,然后,由支持向量回归（ SVR）方法来学习视觉感知特征与立体图像体验质量得分的关系,最后,通过训练好的SVR模型来预测立体图像体验质量。实验结果表明所提算法能够有效地预测立体图像体验质量。     

4K超高清节目制播质量的控制策略

发展4K超高清视频产业是新时期电视台制作电视节目的主要趋势,是促进广播电视台创新发展的关键。但电视台在推进4K超高清节目制播工作时,还需合理应用4K技术,保障4K超高清节目的制播质量,提升电视台的市场竞争力。因此,文章结合4K超高清节目的技术规范,分析了当前4K超高清节目制播中存在的问题,提出了4K超高清节目制播质量的控制策略,从而保障广播电视台4K超高清节目的整体质量。     

4K超高清电视播出系统设计分析

超清晰画质、图像层次丰富以及还原自然的色彩,是4K超高清(Ultra High Definition,UHD)技术在节目制作与转播方面中应用所表现出的极为鲜明的特点.不断提高的科学技术水平为4K超高清播出系统的进一步发展和应用提供了有利条件,因此,可以在原有播出系统架构基础上融合4K超高清技术,设计全新的播出系统,为用户提供更加清晰的视觉体验.基于此,对4K超高清电视播出系统设计原则展开研究,着重分析4K超高清电视播出系统设计过程涉及到的关键技术,希望为4K超高清技术在电视行业中的成熟化应用提供一些建议.     

4K超高清传输及制作技术

科学发展日新月异,人们对感官的视听体验提出了更高的要求,传统的标清电视和高清电视逐渐无法满足人们的视觉体验要求,新的4K超高清技术应运而生.电子和信息行业技术快速发展,加上相关政策标准的完善,对4K技术的发展和推广提供了强大的动力.基于此,详细介绍4K超高清传输技术和制作技术的发展及相关可行性方案.     

No-reference stereoscopic image quality evaluator based on human visual characteristics and relative gradient orientation

Stereoscopic image quality assessment (SIQA) is of great significance to the development of modern three-dimensional (3D) display technology. In this work, by further mining the relationship between visual features and stereoscopic image quality perception, we build a new no-reference SIQA model, which combines the monocular and binocular features. Statistical quality-aware structural features from relative gradient orientation (RGO) map and texture features from the histogram of the weighted local binary pattern (LBP) in the texture image (TLBP) are not only extracted from both monocular view, but also extracted from binocular views to predict binocular quality perception. Meanwhile, the color statistical features ignored by most models and the binocularity feature is extracted to complement the monocular features and the above binocular features, respectively. Finally, all the extracted features and subjective scores are used to predict the objective quality score through the support vector regression (SVR) model. Experiments on four popular stereoscopic image databases show that the proposed model achieves high consistency with subjective assessment, and the performance of the model is very competitive with the latest models.     

Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features

With the development of information technologies, various types of streaming images are generated, such as videos, graphics, Virtual Reality (VR)/omnidirectional images (OIs), etc. Among them, the OIs usually have a broader view and a higher resolution, which provides human an immersive visual experience in a head-mounted display. However, the current image quality assessment works cannot achieve good performance without considering representative human visual features and visual viewing characteristics of OIs, which limited OIs’ further development. Motivated by the above problem, this work proposes a blind omnidirectional image quality assessment (BOIQA) model based on representative features and viewport oriented statistical features. Specifically, we apply the local binary pattern operator to encoder the cross-channel color information, and apply the weighted LBP to extract the structural features. Then the local natural scene statistics (NSS) features are extracted by using the viewport sampling to boost the performance. Finally, we apply support vector regression to predict the OIs’ quality score, and experimental results on CVIQD2018 and OIQA2018 Databases prove that the proposed model achieves better performance than state-of-the-art OIQA models.     

On the use of a scanpath predictor and convolutional neural network for blind image quality assessment

Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution neural network. To exploit the saliency information, only the more perceptually relevant patches that impact subjective judgment more, are considered. To this end, we first compute the saliency map of the distorted image. Then, a scanpath predictor that aims to mimic the visual behavior is employed as patch selector. Finally, a CNN model is used to predict the quality score through the extracted patches. To the best of our knowledge this is the first study to associate a scanpath prediction method and CNN to assess the quality of 2D images. Four CNN models were compared (AlexNet, VGG16, VGG19 and ResNet50) and the performance of the best CNN was compared to the state-of-the-art on four datasets. Experimental results demonstrated the efficiency of the proposed approach and its generalization capacity.     

Screen content image quality assessment based on convolutional neural networks

Screen content image (SCI) is a composite image including textual and pictorial regions resulting in many difficulties in image quality assessment (IQA). Large SCIs are divided into image patches to increase training samples for CNN training of IQA model, and this brings two problems: (1) local quality of each image patch is not equal to subjective differential mean opinion score (DMOS) of an entire image; (2) importance of different image patches is not same for quality assessment. In this paper, we propose a novel no-reference (NR) IQA model based on the convolutional neural network (CNN) for assessing the perceptual quality of SCIs. Our model conducts two designs solving problems which benefits from two strategies. For the first strategy, to imitate full-reference (FR) CNN-based model behavior, a CNN-based model is designed for both FR and NR IQA, and performance of NR-IQA part improves when the image patch scores predicted by FR-IQA part are adopted as the ground-truth to train NR-IQA part. For the second strategy, image patch qualities of one entire SCI are fused to obtain the SCI quality with an adaptive weighting method taking account the effect of the different image patch contents. Experimental results verify that our model outperforms all test NR IQA methods and most FR IQA methods on the screen content image quality assessment database (SIQAD). On the cross-database evaluation, the proposed method outperforms the existing NR IQA method in terms of at least 2.4 percent in PLCC and 2.8 percent in SRCC, which shows high generalization ability and high effectiveness of our model.     

基于双目特征联合的无参考立体图像质量评价

通过模拟人类视觉系统(HVS)的双目视觉行为,提 出一种基于双目特征联合的无参考立 体图像质量评价(NR-SIQA)方法。首先分析立体视觉感知中的双目联合行为,提出 可应用于立体图像质量预 测的双目联合模型;然后采用学习和统计分析的方法,分别提取局部和全局特征并联合作 为感知特征; 最后采用机器学习算法,建立特征和质量的关系模型,并结合基于特征的双目联合模型预测 立体图像质量。实验结果表明,本文方法在对称立体图像库上的Pearson线性相关系数(PLCC)和Spearman等级系数(SRCC)高于0.93,在非对称库上高于0.87,优 于现有评价方法。     

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.     

An optimized CNN-based quality assessment model for screen content image

Most existing convolutional neural network (CNN) based models designed for natural image quality assessment (IQA) employ image patches as training samples for data augmentation, and obtain final quality score by averaging all predicted scores of image patches. This brings two problems when applying these methods for screen content image (SCI) quality assessment. Firstly, SCI contains more complex content compared to natural image. As a result, qualities of SCI patches are different, and the subjective differential mean opinion score (DMOS) is not appropriate as qualities of all image patches. Secondly, the average score of image patches does not represent the quality of entire SCI since the human visual system (HVS) is sensitive to image patches containing texture and edge information. In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference (FR) and no-reference (NR) SCI quality assessment to overcome these two problems. The contribution of our algorithm can be concluded as follows: 1) Considering the characteristics of SCIs, a valid network architecture is designed for both NR and FR visual quality evaluation of SCIs, which makes the networks learn the feature differences for FR-IQA; 2) with the consideration of correlation between local quality and DMOS, a training data selection method is proposed to fine-tune the pre-trained model with valid SCI patches; 3) an adaptive pooling approach is employed to fuse patch quality to obtain image quality, owns strong noise robust and effects on both FR and NR IQA. Experimental results verify that our model outperforms both current no-reference and full-reference image quality assessment methods on the benchmark screen content image quality assessment database (SIQAD). Cross-database evaluation shows high generalization ability and high effectiveness of our model.     

基于稀疏表示的立体图像客观质量评价方法

提出了一种基于稀疏表示的立体图像质量评价方法 ,分为训练和测试两个部分。在训练部 分,通过训练不同频带的立体图像获得立体图像的稀疏字典;在测试部分,根据稀疏字典计 算得到立体图 像的稀疏特征,定义了稀疏特征相似度衡量原始和失真图像信息的差异,并根据稀疏字典计 算了频带增益和左右视点的融合权值,最后融合稀疏特征相似度作为立体图像质量的 客观评价值。在立体图像测试库上的实验结果表明,本文方法的评价结果与主观评价结果有 较好的相关性,符合人类视觉系统的感知。     

Reduced reference image quality assessment using regularity of phase congruency

In this paper, a reduced-reference image quality assessment metric is proposed, which measures the difference of the regularity of the phase congruency (PC) between the reference image and the distorted image. The proposed model adopts a three-stage approach. The PC of the image is first extracted, then the fractal dimensions are computed on PC as the image features that characterize the image structures from the view of the spatial distribution. Finally the image features are pooled as the quality score using ℓ₁ distance. The proposed approach is evaluated on seven public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.     

基于非局部自相似图像块字典学习的伪CT图像预测

随着PET/CT技术的日益发展,其被广泛应用于现代放射治疗。但在采集数据过程中,对人体放射时间较长,辐射当量较大,增加了患者的痛苦,因此人们希望减少CT扫描中X射线的辐射。为解决这一问题,本文提出基于非局部自相似图像块字典学习的伪CT图像预测方法。首先,对训练CT与MRI图像进行图像分块,通过块匹配算法聚类CT图像块,并提取CT与MRI图像块的多尺度特征。其次,通过字典学习,获得MRI图像与CT图像的映射关系矩阵,并对CT图像块进行预测。最后,通过重构算法,从目标MRI图像中得到预测CT图像。仿真实验证明了提出算法相对基于图谱集算法的有效性,以及在现代放射治疗中利用MRI图像替代CT图像的应用前景。