视差与空间频率对自由立体显示器观看舒适度的影响

观看舒适度是评价自由立体显示器性能的重要指标。立体图像的视差和空间频率影响人眼的感知深度,从而对观看舒适度有重要作用。本文采用3DS MAX三维建模软件和Photoshop图像处理软件得到不同视差和空间频率的立体图像,通过观看舒适度的客观度量结合主观评价确定视差及空间频率与观看舒适度的关系,得出观看舒适度较高时的视差范围和与之相对应的空间频率范围。实验表明,立体图像低空间频率所致的模糊可以增加观看舒适度。     

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。     

Depth-enhanced mobile robot teleguide based on laser images

3D stereoscopic visualization may provide a user with higher comprehension of remote environment in teleoperation when compared to 2D viewing. Works in the literature have addressed the contribution of stereo vision to improve perception of some depth cues often for abstract tasks, and it is hard to find contributions specifically addressing mobile robot teleguide. The authors of this paper have investigated stereoscopic viewing in mobile robot teleguide based on video images in a previous work and pointed out advantages of stereo viewing in this type of application as well as shortcomings inherent to the use of visual sensor, e.g. image transmission delay. The proposed investigation aims at testing mobile robot teleguide based on a different sensor: the laser sensor. The use of laser is expected to solve some problems related to visual sensor while maintaining the advantage of having stereoscopic visualization of a remote environment. A usability evaluation is proposed to assess system performance. The evaluation runs under the same setup of the previous study so to have an experimental outcome comparable to the previous one. The evaluation involves several users and two different 3D visualization technologies. The results show a strong improvement in users’ performance when mobile robot teleguide based on laser sensor is (depth-) enhanced by stereo viewing. Some differences are detected between the use of laser and visual sensor which are discussed.     

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。     

立体图像视差自适应调整算法

针对不同尺寸立体显示器播放立体视频时立体视觉效果存在的差异,提出一种通过调节图像水平视差改善立体显示效果的方法。首先应用立体匹配算法得到立体图像初始视差,通过图像分割与连通性检测从中可靠地获取水平视差范围;为保证图像中全部景物在舒适立体融像区内成像,根据显示器尺寸与观看距离,调整左右眼图像在原立体视频中相对位置,形成合理的水平视差,从而在立体显示器上产生舒适的立体视觉效果。实验证明,本文提出的方法可自适应地调整立体图像的水平视差并较好地改善立体显示效果。     

基于视觉显著性的立体显示灰阶串扰量化

为了更为合理地评价立体显示系统中的串扰现象,对传统灰阶串扰进行了加权并进一步单一数值量化。研究选取了大量涵盖各种类型和场景的立体图像,对每一灰阶转换组合出现的概率进行统计,同时提取立体图像视觉显著性,提出了平均灰阶转换显著性的概念。平均灰阶转换显著性以矩阵形式呈现,应用其对传统灰阶串扰矩阵加权后可获得更为合理的灰阶串扰。加权后的灰阶串扰矩阵求和后即可获得数值范围在0%~100%的单一数值串扰。提出的加权灰阶串扰及其数值单一化方法充分考虑了图像内容和人眼视觉关注机理的影响,得到的评价结果更加客观、合理。同时,研究结果对串扰现象的优化以及视觉舒适度的提高具有指导和推动作用。     

利用人眼视觉特性的图像结构差异性杂波度量

针对光电图像杂波效应对目标获取性能的影响,提出一种基于人眼视觉特性的图像结构差异性杂波度量。依据人眼对图像结构的高度自适应性以及图像质量评估领域广泛认可的图像结构的定义方法,计算目标和杂波的结构相似性;并根据人眼的视觉显著性原理,给出两种适用于结构相似性的显著性加权信息度量。采用外场实验对该杂波度量的合理性进行验证,得出基于该杂波度量的目标预测性能与外场目标获取性能,在均方根误差、相关性方面都优于现有的杂波度量的预测性能。     

3D saliency detection based on background detection

Unlike 2D saliency detection, 3D saliency detection can consider the effects of depth and binocular parallax. In this paper, we propose a 3D saliency detection approach based on background detection via depth information. With the aid of the synergism between a color image and the corresponding depth map, our approach can detect the distant background and surfaces with gradual changes in depth. We then use the detected background to predict the potential characteristics of the background regions that are occluded by foreground objects through polynomial fitting; this step imitates the human imagination/envisioning process. Finally, a saliency map is obtained based on the contrast between the foreground objects and the potential background. We compare our approach with 14 state-of-the-art saliency detection methods on three publicly available databases. The proposed model demonstrates good performance and succeeds in detecting and removing backgrounds and surfaces of gradually varying depth on all tested databases.     

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。     

基于区域增长的遥感影像视觉显著目标快速检测

针对传统视觉注意模型在遥感影像视觉显著区域检测中存在的计算复杂度高、检测精度低等缺点,提出了一种新的视觉显著区域快速检测算法。首先利用整数小波变换降低遥感影像的空间分辨率,从而降低视觉注意焦点检测的计算复杂度;然后在视觉特征融合中引入二维离散矩变换,生成边缘与纹理信息更为丰富的遥感影像显著图;最后在显著图分析中提出区域增长策略来获得视觉显著区域的精确轮廓。实验结果表明,新算法不仅有效降低了遥感影像视觉显著区域检测的计算复杂度,而且能够精确描述视觉显著区域的轮廓信息,同时避免了对整幅遥感影像的分割与特征提取,为今后的遥感影像目标检测提供了一定地参考价值。     

基于HSI模型的立体电视图像视觉舒适度评价方法

舒适度是立体显示技术的重要指标之一。针对立体电视图像舒适度评价问题,利用HSI模型分解立体电视图像,分析了立体电视图像色调与舒适度之间的数据关系。通过实验者的人眼主观测试,实验结果表明：按照最小间隔100将立体电视图像分割成36对新图像,比较系统的总结出了立体电视图像色调值与人眼舒适度的数据关系,并给出了有效舒适度立体电视图像的色调值范围。     

一种基于立体视觉显著性的多视点视频比特分配方法

针对多视点立体视频压缩编码,提出了一种基于立 体视觉显著性的比 特分配方法。研究综合利用多视点立体视频数据中场景的运动、深度以及深度边缘信息提取 人眼感兴趣区 域(ROI)的方法;然后根据ROI的划分结果优化区域比特分配。实验结果表 明,本文提出的算法能有效提 高ROI区域的编码性能,同时整体视频的率失真性能有一定程度的提高。     

对象引导的单幅散焦图像深度提取方法

2D图像转3D图像是解决3D影视内容缺乏的主要手段之一,而深度提取是其中的关键步骤.考虑到影视作品中存在大量散焦图像,提出单幅散焦图像深度估计的方法:首先通过高斯卷积将散焦图像转换成两幅模糊程度不同的图像;其次计算这两幅图像在边缘处的梯度幅值比例,进而根据阶跃信号与镜头的卷积模型得到边缘处的模糊度;再次将边缘处的模糊度转换成图像的稀疏深度并利用拉普拉斯矩阵插值得到稠密深度图;最后通过图像的视觉显著度提取前景对象,建立对象引导的深度图优化能量模型,使前景的深度趋于一致并平滑梯度较小区域的深度.该方法利用对象引导的深度优化,剔除了拉普拉斯矩阵插值引入深度图的纹理信息.模拟图像的峰值信噪比和真实图像的视觉对比均表明该算法比现有方法有较大改善.     

An enhanced image quality assessment by synergizing superpixels and visual saliency

Superpixel and saliency-based evaluation methods play important roles in full reference image quality assessment (FR IQA). However, we find that these methods have one complementary principle and three limitations: (1) the weighted maps of superpixel-based methods conflict with the perception of the human visual system; (2) saliency-based methods are inefficient in terms of the block distortion; (3) the general two-direction gradient extraction factor must be extended to be multidirectional. To address these limitations, we propose an enhanced image quality assessment by synergizing superpixels and visual saliency. Specifically, the calculation of a newly proposed framework involves three similarities and two strategies: the saliency, superpixel and multidirectional gradient similarities of the neighborhoods, and the saliency pooling strategy, the fusion strategy of these similarities. Theoretical analysis and experimental results show that the proposed method can effectively address the limitations noted above and outperform the existing methods.     

Stereoscopic video quality assessment based on visual attention and just-noticeable difference models

With the consideration that incorporating visual saliency information appropriately can benefit image quality assessment metrics, this paper proposes an objective stereoscopic video quality assessment (SVQA) metric by incorporating stereoscopic visual attention (SVA) to SVQA metric. Specifically, based upon the multiple visual masking characteristics of HVS, a stereoscopic just-noticeable difference model is proposed to compute the perceptual visibility for stereoscopic video. Next, a novel SVA model is proposed to extract stereoscopic visual saliency information. Then, the quality maps are calculated by the similarity of the original and distorted stereoscopic videos’ perceptual visibility. Finally, the quality score is obtained by incorporating visual saliency information to the pooling of quality maps. To evaluate the proposed SVQA metric, a subjective experiment is conducted. The experimental result shows that the proposed SVQA metric achieves better performance in comparison with the existing SVQA metrics.     

A heuristic framework for perceptual saliency prediction

Saliency prediction can be regarded as the human spontaneous activity. The most effective saliency model should highly approximate the response of viewers to the perceived information. In the paper, we exploit the perception response for saliency detection and propose a heuristic framework to predict salient region. First, to find the perceptually meaningful salient regions, an orientation selectivity based local feature and a visual Acuity based global feature are proposed to jointly predict candidate salient regions. Subsequently, to further boost the accuracy of saliency map, we introduce a visual error sensitivity based operator to activate the meaningful salient regions from a local and global perspective. In addition, an adaptive fusion method based on free energy principle is designed to combine the sub-saliency maps from each image channel to obtain the final saliency map. Experimental results on five natural and emotional datasets demonstrate the superiority of the proposed method compared to twelve state-of-the-art algorithms.     

Virtual Control of Optical Axis of the 3DTV Camera for Reducing Visual Fatigue in Stereoscopic 3DTV

In stereoscopic television, there is a trade‐off between visual comfort and 3‐dimensional (3D) impact with respect to the baseline‐stretch of a 3DTV camera. It is necessary to adjust the baseline‐stretch at an appropriate distance depending on the contents of a scene if we want to obtain a subjectively optimal quality of an image. However, it is very hard to obtain a small baseline‐stretch using commercially available cameras of broadcasting quality where the sizes of the lens and CCD module are large. In order to overcome this limitation, we attempt to freely control the baseline‐stretch of a stereoscopic camera by synthesizing the virtual views at the desired location of interval between two cameras. This proposed technique is based on the stereo matching and view synthesis techniques. We first obtain a dense disparity map using a hierarchical stereo matching with the edge‐adaptive multiple shifted windows. Then, we synthesize the virtual views using the disparity map. Simulation results with various stereoscopic images demonstrate the effectiveness of the proposed technique.     

基于视觉对比机制的红外弱小目标检测方法

为解决复杂背景下红外弱小目标检测精度低的问题,本文提出一种基于视觉对比机制的红外弱小目标检测方法,算法主要模拟了人眼对目标对比度敏感这一机制。首先利用8向梯度方程提取红外图像的梯度显著图并二值化处理;根据小目标的尺寸大小特征对梯度显著图进行优化处理,剔除孤立的噪声点和尺寸较大的背景梯度显著区域;利用视觉对比机制对优化后的显著图进行局部对比度计算,通过阈值处理剔除虚警目标,完成红外弱小目标检测。仿真实验表明,该算法在低信噪比情况下对红外弱小目标的检测率较高,且虚警率低,单帧检测时间较小。     

How visual fatigue and discomfort impact 3D-TV quality of experience: a comprehensive review of technological, psychophysical, and psychological factors

The quality of experience (QoE) of 3D contents is usually considered to be the combination of the perceived visual quality, the perceived depth quality, and lastly the visual fatigue and comfort. When either fatigue or discomfort are induced, studies tend to show that observers prefer to experience a 2D version of the contents. For this reason, providing a comfortable experience is a prerequisite for observers to actually consider the depth effect as a visualization improvement. In this paper, we propose a comprehensive review on visual fatigue and discomfort induced by the visualization of 3D stereoscopic contents, in the light of physiological and psychological processes enabling depth perception. First, we review the multitude of manifestations of visual fatigue and discomfort (near triad disorders, symptoms for discomfort), as well as means for detection and evaluation. We then discuss how, in 3D displays, ocular and cognitive conflicts with real world experience may cause fatigue and discomfort; these includes the accommodation–vergence conflict, the inadequacy between presented stimuli and observers depth of focus, and the cognitive integration of conflicting depth cues. We also discuss some limits for stereopsis that constrain our ability to perceive depth, and in particular the perception of planar and in-depth motion, the limited fusion range, and various stereopsis disorders. Finally, this paper discusses how the different aspects of fatigue and discomfort apply to 3D technologies and contents. We notably highlight the need for respecting a comfort zone and avoiding camera and rendering artifacts. We also discuss the influence of visual attention, exposure duration, and training. Conclusions provide guidance for best practices and future research.