采用SJND模型的动态点云感知编码方法

动态点云能有效描述自然场景与3D对象,提供沉浸式视觉体验;但其数据量庞大。需对其进行有效压缩。提出了采用显著性引导的恰可察觉失真(Saliency-guided Just Noticeable Distortion, SJND)模型的动态点云感知编码方法。针对纹理图感知冗余,构建了基于离散余弦变换域的SJND模型,应用于纹理图编码过程中的DCT系数抑制;考虑到相同失真等级下显著区域的几何失真更易被察觉,提出使用投影显著图将几何图进行分层;最后,为不同层级的编码树单元进行自适应量化参数选择和编码。与V-PCC标准方法相比,在保证动态点云视觉质量的前提下,所提出方法提升了动态点云的编码效率。     

基于全局与局部感知特征的三维彩色点云质量评价

作为物理对象在三维空间的有效表示方法,三维彩色点云可以提供丰富的沉浸式视觉体验,但在其获取、处理、编码传输等各环节会引入失真,从而导致其视觉质量下降.因此,如何监测彩色点云的视觉质量是一个亟待解决的重要问题.本文将三维彩色点云投影到二维平面,提出了一种基于全局与局部感知特征的彩色点云视觉质量评价方法.首先,将三维彩色点云转化为彩色纹理投影图与几何投影图.然后,根据三维彩色点云的纹理与几何失真在其投影图中的不同表象,分别描述并提取其失真特征;其中,在彩色纹理投影图中提取全局颜色与局部纹理特征,在几何投影图中提取全局与局部几何特征.最后,将所有全局和局部感知特征构成最终的特征向量预测彩色点云的视觉质量.在两个主观评价数据库(SJTU-PCQA、CPCD2.0)进行测试的实验结果表明,所提出方法在性能上优于13个现有代表性视觉质量评价方法,与主观感知质量有更好的一致性.     

基于海量点云数据的输电线路三维建模研究

为保障电网输电线路管理的规范化与精益化，设计了基于海量点云数据的输电线路三维建模方法。首先采集输电线路原始点云数据，对其进行预处理，根据点云数据提取输电线路轮廓线，然后采用3D MAX软件构建输电线路的完整三维几何模型，最后通过纹理映射此模型获取到最终输电线路三维模型。结果表明，该方法可精准拼接原始点云数据，有效去除点云数据的噪声点及障碍物等，提升点云数据的整体质量，可为电网输电线路规范化管理提供有效帮助。     

基于激光三维点云技术的3D景观建模设计

为提升城市园林等类型景观的建模精度,以激光三维点云为技术基础,设计一种三维景观建模方法。采用激光三维点云立体式非接触测量技术,获取景观表面数据点三维坐标,在一个坐标系内统一化各角度点云数据,将顺序点间的最远距离作为滤波标准,设定超过标准点为固定端点,平滑处理图像点云,采用三角形网格参数化策略,映射三维网格模型至二维平面中,取得特征点纹理坐标,利用调和映射算法求解非约束点的纹理坐标,通过自适应部分调整策略,优化点云数据纹理,得到最终的景观模型。试验采集研究区域中一处景观的三维数据,结合景观模型效果与评估指标值得出,所提方法能够有效建立模型,且精准度较高,模型细节信息保存得相对完整。     

复杂场景点云数据的6D位姿估计深度学习网络

针对工业上常见的弱纹理、散乱摆放复杂场景下点云目标机器人抓取问题,该文提出一种6D位姿估计深度学习网络。首先,模拟复杂场景下点云目标多姿态随机摆放的物理环境,生成带真实标签的数据集;进而,设计了6D位姿估计深度学习网络模型,提出多尺度点云分割网络(MPCS-Net),直接在完整几何点云上进行点云实例分割,解决了对RGB信息和点云分割预处理的依赖问题。然后,提出多层特征姿态估计网(MFPE-Net),有效地解决了对称物体的位姿估计问题。最后,实验结果和分析证实了,相比于传统的点云配准方法和现有的切分点云的深度学习位姿估计方法,所提方法取得了更高的准确率和更稳定性能,并且在估计对称物体位姿时有较强的鲁棒性。     

基于CAFF-PointNet的机载LiDAR点云语义分割

针对机载LiDAR点云中几何结构复杂和不同地物尺度变化大导致小目标点云分类准确率低的问题,本文提出了一种基于通道注意力机制进行多尺度特征融合的卷积神经网络.首先,通过球形邻域计算点云的平面度、线性度、法向量以及本征熵等浅层几何特征,并将其与网络提取的深层次语义特征进行融合,增强模型对点云几何结构的感知能力;其次,设计基于通道注意力机制的多尺度特征融合模块,学习特征融合的权重系数,使网络可以自适应调整不同尺度目标的感受野大小,实现对不同尺度信息的筛选,进而提高小尺度目标的分类精度.实验结果表明,与其他模型相比,本文模型在ISPRS机载LiDAR点云上的平均F1分数为72.2％,且对电力线和汽车类别均取得了最高的分类精度,F1分数分别为64.3％和79.9％.     

基于点云迁移的人体点云位置及颜色补全

由于三维扫描设备获取的点云存在目标遮挡、设备视角等问题,致使点云有所缺失。点云补全是为下游任务进行点云预处理的重要工作。然而现有的点云补全工作集中在对简单物体的补全上,缺乏对较复杂人体点云补全的研究。为将点云补全任务引入下游人体重建相关任务中,本文提出了一种由粗到精的人体点云预测方法,对人体位置及颜色缺失信息补全。通过创建具有精细服饰纹理的人体点云数据集,采用点云迁移的网络结构,充分利用点云全局粗糙信息,结合多层网络预测完整点云,优化了上采样方案;然后,在上采样后的点云上进行了颜色补全,通过部分点云颜色信息进行特征值扩散和提取;最后,通过一个多层感知机进行颜色预测。在人体数据集上的实验结果表明,该方法相比现有主流方法不仅在客观指标上具有更好的表现,主观质量上也能保证获得更加完整、边缘清晰的点云。     

基于引导调制的彩色点云无参考质量评价方法

彩色点云(color point cloud, CPC)作为三维场景和对象的有效描述形式,在虚拟现实、增强现实等许多领域得到重要应用。CPC在其采集、压缩、传输、重建等过程中会引入相应的失真,需要设计有效的评价方法对失真CPC质量进行评测。本文提出一种基于引导调制的CPC无参考质量评价方法。考虑到几何信息与彩色纹理信息的联合失真,利用引导调制的方法联立两者,以综合考虑几何失真、彩色纹理失真、联合失真。结合人眼的多通道性,利用剪切波变换提取特征。最后,将所有特征构成的特征向量输入到支持向量回归模型(support vector regression, SVR)学习预测点云质量。实验结果表明,所提出的方法与人类主观感知具有很好的一致性。     

基于脉冲耦合神经网络的点云曲面去噪

 提出一种基于脉冲耦合神经网络(PCNN)的点云曲面去噪算法.该算法主要分为两步:噪声点定位和噪声点滤波.首先针对点云曲面构建一个PCNN神经网络,各个神经元的外部刺激值由邻近点的几何位置差异和法向差异构成,利用神经元输出的自适应点火捕获特性,实现了噪声点的定位;而后针对点云曲面中的噪声点,基于网格光顺中双边滤波的思想,实现噪声点的滤波,对于非噪声点,则保持原有的几何位置不变.实验结果表明,由于区分了噪声点和非噪声点,该算法较传统的点云曲面去噪算法能更加有效的去除噪声的同时并保持模型的几何特征.     

三维散乱点云模型的特征点检测

随着三维点云模型越来越受到人们的关注,如何对数据量大,无序的三维点云模型进行特征点检测也是近几年的研究热点。本文提出了基于曲率和密度的特征点检测算法,为每个数据点定义一个特征参数,这个参数由三部分组成:点到邻居点的平均距离;点的法向与邻居点法向夹角的和;数据点曲率。然后通过八叉树方法计算模型的数据点密度,将这个密度作为阈值,特征参数大于阈值的点就是检测到的特征点。本文计算时,检测模型的特征点只需用到三维点云模型的几何特征,如数据点法向,曲率和邻居点。实例验证本算法可准确地检测出散乱数据点云的特征点。      

TGP-PCQA: Texture and geometry projection based quality assessment for colored point clouds

Colored point cloud (PC) will inevitably encounter distortion during its acquisition, processing, coding and transmission, which may affect the visual quality of the colored PC. Therefore, it is necessary to design an effective tool for colored PC quality assessment (PCQA). In this paper, considering the mapping relationship of perception between the colored PC and its corresponding projection images, we propose a novel PCQA method based on texture and geometry projection (denoted as TGP-PCQA). The main idea of the proposed TGP-PCQA method is to obtain texture and geometry projection maps from different perspectives for evaluating the colored PC. Specifically, 4D tensor decomposition is used to obtain the combination and difference information between the reference and distorted texture projection maps for mainly characterizing texture distortion of colored PC. Meanwhile, the edge features of the geometry projection map are calculated to measure the global or local geometry distortion. All of the extracted features are combined to predict an overall quality of colored PC. In addition, this paper establishes a multi-distorted colored PC database named CPCD2.0 with compression distortions and Gaussian noise, which orients to the influence of both geometry and texture components in distortion. Experimental results on two open subjective evaluation databases (IRPC and SJTU-PCQA) and the self-built CPCD2.0 database show that the proposed TGP-PCQA method outperforms the state-of-the-art PCQA methods. We are also providing the self-built CPCD2.0 database free of charge at https://github.com/cherry0415/CPCD2.0.     

Content-adaptive bitstream-layer model for coding distortion assessment of H.264/AVC networked video

Bitstream-layer models are designed to use the information extracted from both packet headers and payload for real-time and non-intrusive quality monitoring of networked video. This paper proposes a content-adaptive bitstream-layer (CABL) model for coding distortion assessment of H.264/AVC networked video. Firstly, the fundamental relationship between perceived coding distortion and quantization parameter (QP) is established. Then, considering the fact that the perceived coding distortion of a networked video significantly relies on both the spatial and temporal characteristics of video content, spatial and temporal complexities are incorporated in the proposed model. Assuming that the residuals before Discrete Cosine Transform (DCT) keep to the Laplace distribution, the scale parameters of the Laplace distribution are estimated utilizing QP and quantized coefficients on the basis of the Parseval theorem firstly. Then the spatial complexity is evaluated using QP and the scale parameters. Meanwhile, the temporal complexity is obtained using the weighted motion vectors (MV) considering the variations in temporal masking extent for high motion regions and low motion regions, respectively. Both the two characteristics of video content are extracted from the compressed bitstream without resorting to a complete decoding. Using content related information, the proposed model is able to adapt to different video contents. Experimental results show that the overall performance of CABL model significantly outperforms that of the P.1202.1 model and other coding distortion assessment models in terms of widely used performance criteria, including the Pearson Correlation Coefficient (PCC), the Spearman Rank Order Correlation Coefficient (SROCC), the Root-Mean-Squared Error (RMSE) and the Outlier Ratio (OR).     

基于QDCT和贝叶斯谱熵的盲彩色图像质量评价

图像质量评价算法在评价彩色图像质量时,往往会因损失色彩信息或者破坏彩色图像结构的整体性,而使得评价结果与人眼观测结果不一致.由于图像越模糊其频谱的高频分量分布越不均匀,基于四元数离散余弦变换(QDCT)和贝叶斯谱熵,提出了一种无参考模糊彩色图像质量评价算法.首先,利用四元数矩阵对彩色图像进行表示并分解成不重叠的8×8 ...     

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.     

面向UHDTV的多分辨率视频显示质量评估研究

超高清电视(UHDTV,Ultra High Definition TV)凭借其超高的屏幕分辨率可以更细腻地展现视频的内容,受到越来越多用户的青睐,然而目前超高清节目源发展有限,通常只能将低于超高清电视屏幕分辨率的视频经过插值后播放,这样处理可能会对用户主观体验带来一定的影响。如何准确评估UHDTV多分辨率视频源对视频显示质量的影响,对UHDTV业务的部署和优化起到指导性的作用。本文通过主观实验,利用SSIM算法预测视频源编码质量,并进一步分析视频源分辨率对UHDTV视频显示质量的影响,提出一种面向UHDTV的视频显示质量评估模型。实验结果表明,相比SSIM、VIFp和MOVIE三种经典的视频质量评估算法,本文模型所得视频显示质量和主观质量之间的皮尔森系数分别提高1.13%、1.89%和2.31%,斯皮尔曼等级相关系数分别提高1.35%、1.53%和2.24%,RMSE降低35.05%、36.94%和39.71%。所提模型可以更加准确地评价UHDTV视频显示质量。     

Perceptually-lossless image coding based on foveated-JND and H.265/HEVC

Removing perceptual redundancy plays an important role in image compression. In this paper we develop a foveated just-noticeable-difference (FJND) model to quantify the perceptual redundancy in the image and integrate it in the H.265/HEVC intra encoding framework to provide a perceptually lossless image coding solution. Different to the conventional JND models, our proposed FJND model considers the relationship between contrast masking effect and the foveation properties of HVS. Furthermore, to achieving the perceptually lossless coding, the FJND model is integrated in the H.265/HEVC framework by determining the quantization parameter to ensure that the resulting distortion is no larger than the FJND threshold. The experiments demonstrate that the proposed method effectively improves the compression performance.     

Wavelet-based watermarking for color images through visual masking

In this paper, a wavelet-based watermarking scheme for color images is proposed. The watermarking scheme is based on the design of a color visual model that is the modification of a perceptual model used in the image coding of gray scale images. The model is to estimate the noise detection threshold of each wavelet coefficient in luminance and chrominance components of color images in order to satisfy transparency and robustness required by the color image watermarking technique. The noise detection thresholds of coefficients in each color component are derived in a locally adaptive fashion based on the wavelet decomposition, by which perceptually significant coefficients are selected and a perceptually lossless quantization matrix is constructed for embedding watermarks. Performance in terms of robustness and transparency is obtained by embedding the maximum strength watermark while maintaining the perceptually lossless quality of the watermarked color image. Simulation results show that the proposed scheme is more robust than the existing scheme while retaining the watermark transparency.     

Wavelet-based lossless compression of coronary angiographic images

The final diagnosis in coronary angiography has to be performed on a large set of original images. Therefore, lossless compression schemes play a key role in medical database management and telediagnosis applications. This paper proposes a wavelet-based compression scheme that is able to operate in the lossless mode. The quantization module implements a new way of coding of the wavelet coefficients that is more effective than the classical zerotree coding. The experimental results obtained on a set of 20 angiograms show that the algorithm outperforms the embedded zerotree coder, combined with the integer wavelet transform, by 0.38 bpp, the set partitioning coder by 0.21 bpp, and the lossless JPEG coder by 0.71 bpp. The scheme is a good candidate for radiological applications such as teleradiology and picture archiving and communications systems (PACS's).     

基于双目能量响应的无参考立体图像质量评价

为了实现对不同失真类型立体图像的质量评价,提出了一种基于双目能量响应的无参考立体图像质量评价(NR-IAQ)方法。首先,通过对各失真图像进行Gabor滤波,提取出不同频率、不同方向、不同视差响应下的局部特征矢量,作为立体图像特征信息;然后,利用支持向量回归(SVR)建立立体图像特征与主观评价值的关系,从而预测得到立体图像质量的客观评价值。实验结果表明,对于NBU-3D测试库,Pearson线性相关系数值在0.92以上,Spearman等级相关系数值在0.93以上;对于LIVE-3D测试库,Pearson线性相关系数值在0.96以上,Spearman等级相关系数值在0.96以上;与现有的全参考(FR)和(NR)质量评价方法相比,本方法得到的客观评价值与主观评价结果有较好的相关性,更加符合人眼视觉系统。     

基于人类视觉的感知立体图像质量评价方法

为了实现对不同失真类型的立体图像进行质量评 价,提出了一种基于人类视觉的立体图像 质量客观评价方法,分别从图像清晰度与立体感两方面进行评价。图像清晰度方面,将原始 与失真立体图 像分解为5个带通图像后利用对比度敏感度函数(CSF)优化各失真带通图像,并模拟掩 盖效应,通过整合各原始 带通图像,综合感知误差,构造信噪比(SNR)作为评价图像 清晰度的性能指标;立体感方面,对绝对差值图像进 行视觉感知模拟,建立SNR指标评价立体感的优劣。实验结 果表明,对不同失真类型立体图像的评价 结果表明,Pearson线性相关系数(PLCC)与Spearman等级 相关系数(SRCC)均优于现有评价方法。