Stereoscopic 3D Video Research Articles

“Instant 3D” Angiography: Novel Technique for Rapid Conversion of 2D Angiograms into 3D Stereoscopic Videos

Rotational angiography, often referred to as a "spin", is typically presented in 2D. Since rotational angiograms are composed of images acquired from multiple angles, we took advantage of this property to develop a method for converting any rotational angiogram into a 3 dimensional (3D) video. Our aim was to develop a low cost and easily distributable solution without requiring additional hardware or altering acquisition techniques. Six previously acquired rotational angiograms from our institution were imported using custom-written code and exported as anaglyph (red-cyan) videos. The resulting 3D videos convey anatomical depth that is not apparent from viewing the 2D images alone. Processing time was 1.3±0.6s (mean±SD) per angiogram. The only associated cost was $10 for red-cyan 3D glasses. Using our software, any rotational angiogram with at least 0.3 frames per degree of rotation can be converted into 3D. Our solution is an inexpensive and rapid method for generating stereoscopic videos from existing angiograms. It does not require any additional hardware and is readily deployable in low-resource settings. Because the videos are in anaglyph format, they are viewable on any 2 dimensional (2D) display in the interventional suite or operating room, on a mobile device, or at home.

Examination of gaze behaviour in social anxiety disorder using a virtual reality eye-tracking paradigm: protocol for a case–control study

IntroductionSocial anxiety disorder (SAD) has an early onset, a high lifetime prevalence, and may be a risk factor for developing other mental disorders. Gaze behaviour is considered an aberrant feature...

An Indispensable Revolution For Pakistan, The Beginning Of Robot Assisted MIMVS.

Madam, Minimally invasive surgical (MIS) techniques or ‘sternal sparing’ strategies, performed by making minor right chest incisions, were introduced in the mid-1990s [1,3]. From that point forward, there has been a decrease in surgical injury and postoperative recuperation, coming about in expanded acknowledgement of these approaches. It works on three essential standards: rebuilding and conserving MV pamphlet portability, creating a vast flyer coaptation surface, and remodeling of the mitral annulus to supply an ideal and steady orifice area. [1] The history of MIMVS expanded from the 1990s when minimally invasive approaches such as the parasternal incision, hemi sternal incision and the mini thoracotomy were investigated by autonomous groups led by Delos Cosgrove and Lawrence Cohn. Further advancements include the primary video-directed repair and substitutions performed through a mini-thoracotomy and the primary utilization of end balloon clamping. Later, improvements incorporated the coming of stereoscopic 3D video endoscopy and mechanical surgery. [5] Robotic technology facilitates complex surgical procedures on the mitral valve without opening the chest. The most significant advantage is diminished surgical injury, which deciphers into early recuperation. In 1998, robotic mitral valve repair was initiated and adopted in 2005 by the US following the first multicenter trial. In classic robotic mitral valve surgery, femoral-femoral cardiopulmonary machine cannulation is performed through a small incision in the groin. The surgeon leading the robotic mitral valve surgery must have the relevant heart surgery certification from the country's certifying institution. The console surgeon should be experienced in all aspects of mitral valve surgery and be highly versed in repair techniques as robotic procedures take longer than traditional surgeries, especially during the learning curve. Apprenticeship in the so-called mini-mitral using conventional videoscopy is also an advantage, but not a mandatory requirement The courses on robotic mitral valve surgery including formal didactic and practice-oriented training should be obtained by the lead surgeon and the team. Virtual stimulators and wet labs should be provided by the hospital to the team to practice their first 8-10 cases until they gain expertise ---Continue

Binocular Rivalry Impact on Macroblock-Loss Error Concealment for Stereoscopic 3D Video Transmission

Three-dimensional video services delivered through wireless communication channels have to deal with numerous challenges due to the limitations of both the transmission channel's bandwidth and receiving devices. Adverse channel conditions, delays, or jitters can result in bit errors and packet losses, which can alter the appearance of stereoscopic 3D (S3D) video. Due to the perception of dissimilar patterns by the two human eyes, they can not be fused into a stable composite pattern in the brain and hence try to dominate by suppressing each other. Thus, a psychovisual sensation that is called binocular rivalry occurs. As a result, undetectable changes causing irritating flickering effects are seen, leading to visual discomforts such as eye strain, headache, nausea, and weariness. This study addresses the observer's quality of experience (QoE) by analyzing the binocular rivalry impact on the macroblock (MB) losses in a frame and its error propagation due to predictive frame encoding in stereoscopic video transmission systems. To simulate the processing of experimental videos, the Joint Test Model (JM) reference software has been used as it is recommended by the International Telecommunication Union (ITU). Existing error concealing techniques were then applied to the contiguous lost MBs for a variety of transmission impairments. In order to validate the authenticity of the simulated packet loss environment, several objective evaluations were carried out. Standard numbers of subjects were then engaged in the subjective testing of common 3D video sequences. The results were then statistically examined using a standard Student's t-test, allowing the impact of binocular rivalry to be compared to that of a non-rivalry error condition. The major goal is to assure error-free video communication by minimizing the negative impacts of binocular rivalry and boosting the ability to efficiently integrate 3D video material to improve viewers' overall QoE.

No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System

Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers. Unlike for 2D videos, a widely accepted No-reference objective model for assessing transmitted 3D videos that explores the Human Visual System (HVS) appropriately has not been developed yet. Distortions perceived in 2D and 3D videos are significantly different due to the sophisticated manner in which the HVS handles the dissimilarities between the two different views. In real-time video transmission, viewers only have the distorted or receiver end content of the original video acquired through the communication medium. In this paper, we propose a No-reference quality assessment method that can estimate the quality of a stereoscopic 3D video based on HVS. By evaluating perceptual aspects and correlations of visual binocular impacts in a stereoscopic movie, the approach creates a way for the objective quality measure to assess impairments similarly to a human observer who would experience the similar material. Firstly, the disparity is measured and quantified by the region-based similarity matching algorithm, and then, the magnitude of the edge difference is calculated to delimit the visually perceptible areas of an image. Finally, an objective metric is approximated by extracting these significant perceptual image features. Experimental analysis with standard S3D video datasets demonstrates the lower computational complexity for the video decoder and comparison with the state-of-the-art algorithms shows the efficiency of the proposed approach for 3D video transmission at different quantization (QP 26 and QP 32) and loss rate (1% and 3% packet loss) parameters along with the perceptual distortion features.

Jomodevi: A joint motion and depth visibility prediction algorithm for perceived stereoscopic 3D quality

In this paper, we introduce a completely blind and unsupervised no-reference model that performs the quality assessment of stereoscopic 3D videos by using joint motion and depth visibility. In order to achieve such objective metric, first we compute the correlation map between the motion vector map and the depth map, and then we study the natural scene statistics of the estimated correlation map. We empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD) and compute the UGGD parameters at multi-scale and multi-orient steerable subband decomposition. It is shown that the estimated UGGD features are capable of discriminating distortions. We then compute the mean vector and the covariance matrix from the estimated UGGD features of the correlation maps of motion vector and the depth maps of undistorted and distorted S3D videos. This is followed by the computation of the Wave Hedges distance between the mean vectors of the undistorted and distorted contents, and the Bhattacharyya distance is measured between the corresponding covariance matrices. We pool these distance measures to achieve the overall joint motion and depth quality of S3D videos. The efficiency of the proposed algorithm is evaluated on the well-known IRCCYN, LFOVIAS3DPh1 and LFOVIAS3DPh2 S3D video datasets. Our model demonstrates robust and consistent performance across all distortion types and shows competitive performance against other 2D and 3D image and video quality assessment algorithms. Furthermore, since the proposed algorithm is completely blind, it does not require any training and testing analysis on the content features and the subjective scores.

Using virtual reality in lumbar puncture training improves students learning experience

BackgroundLumbar puncture (LP) is a commonly performed medical procedure in a wide range of indications. Virtual reality (VR) provides a stimulating, safe and efficient learning environment. We report the design and the evaluation of a three dimensions (3D) video for LP training.MethodsWe recorded a stereoscopic 180-degrees 3D video from two LPs performed in clinical settings in Fernand Widal Lariboisière University Hospital, Paris, France. The video was administered to third-year medical students as well as to a residents and attendings group during LP simulation-based training sessions.ResultsOn 168 participants (108 novice third-year medical students, and 60 residents and attendings with prior LP experience), satisfaction after video exposure was high (rated 4.7 ± 0.6 on a 5-point scale). No significant discomfort was reported (comfort score graded 4.5 ± 0.8 on 5). LP-naive students displayed higher satisfaction and perceived benefit than users with prior LP experience (overall, P < 0.05). Trainees evaluated favorably the 3D feature and supported the development of similar tutorials for other medical procedures (respectively, 3.9 ± 1.1 and 4.4 ± 0.9 on 5).ConclusionWe report our experience with a 3D video for LP training. VR support could increase knowledge retention and skill acquisition in association to LP simulation training.

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery.

Stereo 3D video from surgical procedures can be highly valuable for medical education and improve clinical communication. But access to the operating room and the surgical field is restricted. It is a sterile environment, and the physical space is crowded with surgical staff and technical equipment. In this setting, unobscured capture and realistic reproduction of the surgical procedures are difficult. This paper presents a method for rapid and reliable data collection of stereoscopic 3D videos at different camera baseline distances and distances of convergence. To collect test data with minimum interference during surgery, with high precision and repeatability, the cameras were attached to each hand of a dual-arm robot. The robot was ceiling-mounted in the operating room. It was programmed to perform a timed sequence of synchronized camera movements stepping through a range of test positions with baseline distance between 50-240 mm at incremental steps of 10 mm, and at two convergence distances of 1100 mm and 1400 mm. Surgery was paused to allow 40 consecutive 5-s video samples. A total of 10 surgical scenarios were recorded.

Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset

The use of Stereoscopic 3D (S3D) videos has been popular in commercial markets with ongoing developments in the field of visual entertainment in recent years. A wide variety of projection methods of 3D video content is currently available, such as projection to a panoramic screen and projection of omnidirectional video content from head mounted displays using Virtual Reality (VR) technology. This article investigates the Quality of Experience (QoE) and associated Visually Induced Motion Sickness (VIMS) caused by the viewing of S3D videos. The investigations used three different projection screens: a 3D flat screen, a 3D panoramic screen in a hemispherical shaped room and a VR headset. Several assessment methods including a Simulator Sickness Questionnaire (SSQ), ElectroEncephaloGraphy (EEG), and measurement tools for eye blink rate detection were applied to measure the QoE experienced by viewers. The SSQ scores were also compared with the behavioral data such as attention and meditation levels and enjoyment ratings acquired from different video content and projection screens. The results indicate that the projection screen is a key factor affecting the level of visual fatigue, VIMS and QoE assessments, which are discussed in-depth in the article.

Does 3D stereoscopy support anatomical education?

The teaching of anatomy in medical education has historically been based on lectures, cadaveric dissections, and illustrated books for students. Stereoscopic 3D videos are now easily accessible via smartphone and affordable for students. This study aimed to investigate whether a 3D stereoscopic instruction video could improve learning over 2D video. A prospective controlled study on a single-site was conducted at the University of Angers. Content knowledge was assessed, followed by the presentation of an instructional neuroanatomy video. Participants watched the video in either 3D or 2D format, then they completed an anatomy written test. Pre-video and post-video performances were analyzed with independent t tests on total score, fundamental anatomical knowledge, anatomical relationships and reasoning. 175 subjects completed the study. At baseline, the 3D (n = 91) and 2D (n = 86) groups were similar, in age and class level. 3D and 2D scores were similar in the pre-test session and in the fundamental knowledge post-test (mean 73.2% vs 74.4%, p = 0.37). Average scores for the 3D group were better for the post-test regarding anatomical relationships (mean 86.4% vs. 63.5%, p = 0.004), clinical inference/reasoning (mean 76.8% vs. 67.6%, p = 0.023) and total note (mean 76.8% vs. 67.6%, p = 0.07). Regarding the 3D student's satisfaction questionnaire (n = 91), 70 students (77%) agreed that the stereoscopic video allowed good 3D visualization of anatomical structures. The student enjoyed using the stereoscopic video (n = 75, 82.5%). Most students supported the use of this kind of stereoscopic 3D video in their normal teaching as a complementary tool (n = 78, 85%). The incorporation of 3D videos as ancillary teaching into curricula could be of interest to improve the knowledge of anatomical relationships and reasoning among students.

Modelling effects of S3D visual discomfort in human emotional state using data mining techniques

In recent years, the rapid development of diverse media has been evident in disparate fields such as consumer electronics, automotive infotainment and healthcare software. There is a need for innovative methods to assess user perceived Quality of Experience (QoE), as a proxy for consumer satisfaction with such systems and services. Users emotional state plays a key role in QoE; thus, it is necessary to consider it in user experience evaluation and the design process of stereoscopic 3D video content. In the present article we introduce the use of a specially designed model based on a feedforward Multilayer Perception Artificial Neural Network as an appropriate Machine Learning technique for the estimation of human emotional state while viewing various categories of stereoscopic 3D video content. The goal is to design an emotional state estimator based on direct psychophysiological measurements. The considered psychophysiological signals include heart rate (HR) calculated from an echocardiogram (ECG), electro-dermal activity (EDA), and brain activity (BA) in EEG signals. Participants watched a series of 3D video contents varying in terms of visual quality, while the mentioned psychophysiological signals were recorded, and self-reported subjectively experienced emotions using a Self-Assessment Manikin (SAM) questionnaire. The obtained results show that it is possible to construct such a highly precise estimator of emotional states.

Automatically optimised stereoscopic camera control based on an assessment of 3D video quality of experience

Stereoscopic 3D (S3D) technologies have gained significant attention due to their wide applications. However, producing high-quality S3D content is still a challenging task that requires careful handling to achieve the artistic intent and maintain visual comfort. In this study, we present an automatically controlled stereoscopic camera controller that specifically addresses the challenges in S3D content production. The key idea that distinguishes our method from the existing work is that our method aims to predict the 3D Quality of Experience (QoE) in the production stage so that the optimised camera parameters can be obtained automatically. To this end, considering two interconstraint indicators, i.e., visual comfort and perceived depth, we collect and label a data set of S3D video scene clips and generate a 3D video QoE assessment model that can guide the optimisation of the stereoscopic camera parameters. We describe how to implement our system into a modern production pipeline that has been used in some projects, including commercial ones. The experimental results, including the user studies, demonstrate that our system enhances the perceived depth without creating visual fatigue and that our controller can make the production of S3D content easier and more efficient.

Automatically optimised stereoscopic camera control based on an assessment of 3D video quality of experience

Stereoscopic 3D (S3D) technologies have gained significant attention due to their wide applications. However, producing high-quality S3D content is still a challenging task that requires careful handling to achieve the artistic intent and maintain visual comfort. In this study, we present an automatically controlled stereoscopic camera controller that specifically addresses the challenges in S3D content production. The key idea that distinguishes our method from the existing work is that our method aims to predict the 3D Quality of Experience (QoE) in the production stage so that the optimised camera parameters can be obtained automatically. To this end, considering two interconstraint indicators, i.e., visual comfort and perceived depth, we collect and label a data set of S3D video scene clips and generate a 3D video QoE assessment model that can guide the optimisation of the stereoscopic camera parameters. We describe how to implement our system into a modern production pipeline that has been used in some projects, including commercial ones. The experimental results, including the user studies, demonstrate that our system enhances the perceived depth without creating visual fatigue and that our controller can make the production of S3D content easier and more efficient.

Stereo Vision Based Robot for Remote Monitoring with VR Support

The machine vision systems have been playing a significant role in visual monitoring systems. With the help of stereovision and machine learning, it will be able to mimic human-like visual system and behaviour towards the environment. In this paper, we present a stereo vision based 3-DOF robot which will be used to monitor places from remote using cloud server and internet devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw, pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This video stream is sent to the user through any generic internet devices with VR box support, i.e., smartphones giving the user a First-person real-time 3D experience and transfers the head motion of the user to the robot also in Real-time. The robot will also be able to track moving objects and faces as a target using deep neural networks which enables it to be a standalone monitoring robot. The user will be able to choose specific subjects to monitor in a space. The stereovision enables us to track the depth information of different objects detected and will be used to track human interest objects with its distances and sent to the cloud. A full working prototype is developed which showcases the capabilities of a monitoring system based on stereo vision, robotics, and machine learning.

Combined Inter-layer FEC and Hierarchical QAM for Stereoscopic 3D Video Transmission

We consider an inter-layer FEC (IL-FEC) technique combined with hierarchical QAM for an efficient and reliable transmission of stereoscopic 3D video. The transmit sources are left and right video streams, and one of them is considered a more important stream for guarantee of conventional 2D service. The source node transmits both left and right videos to a destination node by using the IL-FEC and hierarchical 16QAM schemes, in which video peak signal-to-noise ratio (PSNR) performance varies with the hierarchical value $$(\alpha )$$. In this paper, we present the suitable hierarchical values to achieve the target PSNR performances even in low SNR environment. Numerical simulation results show that the proposed system using IL-FEC and hierarchial 16QAM schemes with the suitable $$\alpha$$ value can gain about 7.3–9.3 dB in the average PSNR performance, compared with the system using IL-FEC and conventional 16QAM schemes.

Auto-stereoskopik 3BTV için Öznel Deneyim Kalitesi Modellerinin

Measuring Quality of Experience (QoE) of stereoscopic 3D video is a hot research topic. Subjective models are considered as the most reliable and facilitate development of objective models. However, to collect user opinion scores takes long time. Therefore, new subjective assessment models should be proposed providing not only time-efficiency but also good accuracy and reliability. In this study, two novel subjective QoE models are proposed as alternative to the conventional Double Stimulus Continuous Quality Scale method. Also, a fair comparison method is proposed to evaluate performances of the three subjective methods using the same stimuli prepared with the most recent multi-view video codec on an auto-stereoscopic 3DTV. Correlations are calculated using two objective QoE measures using depth maps and structural similarities. The results demonstrate that the performances of the proposed models are comparable to each other and both models are superior to the conventional method.

Depth perceptual quality assessment for symmetrically and asymmetrically distorted stereoscopic 3D videos

Depth perception is one of the most important features provided by stereoscopic 3D videos, which is also the major difference to 2D videos. Its quality, called depth perceptual quality or depth quality, is one of the key factors that directly affects the quality of experience of 3D videos. Since 3D video shall be compressed, transmitted and reconstructed in 3D video system, distortion introduced from compression and processing affects the video quality as well as the depth perceptual quality. However, understandings of how the distortions affect the depth perception and how to evaluate the depth perceptual quality of 3D video remain limited. In this paper, subjective experiments are firstly conducted for investigating the compression and processing distortions’ impacts on the perceived depth quality of 3D videos. The subjective experiments over the datasets show that the loss of video details can cause the degradation of the depth perceptual quality in monocular and binocular perspectives. In addition, the relationships between video quality and depth quality for both symmetric and asymmetric distorted 3D stereoscopic videos are analyzed. Both the subjective video quality and depth quality scores are released and available for public. Secondly, an objective depth quality assessment algorithm is proposed for measuring the depth perceptual quality degradation of symmetrically and asymmetrically distorted stereoscopic 3D videos. The experimental results demonstrate that the proposed metric has better consistency with the subjective scores of human vision system compared with the state-of-the-art schemes.

Visually Consistent Color Correction for Stereoscopic Images and Videos

In stereoscopic 3D (S3D) color correction, visual inconsistency is a common problem that leads to perceptual quality degradations. In this paper, we propose an S3D image/video color correction strategy that resolves global, local, and temporal color discrepancies simultaneously. We achieve the image-based S3D color correction by three steps: a coarse-grain color correction for global color matching, a fine-grain color correction to further improve both global and local color consistencies, and a guided filtering process to guarantee the structural consistency before and after color correction. In addition, we extend the above strategy to S3D and multiview video color correction. To achieve temporal consistency between successive video frames, we develop an improved histogram matching within a sliding window on time axis. In our method, the mapping functions for each color channel change gradually following the video stream to avoid abrupt temporal changes in colors. The experimental results demonstrate that the proposed strategy outperforms the state-of-the-art color correction algorithms for images and videos.

Immersive 3D exposure-based treatment for spider fear: A randomized controlled trial

Stereoscopic 3D gives the viewer the same shape, size, perspective and depth they would experience viewing the real world and could mimic the perceptual threat cues present in real life. This is the first study to investigate whether an immersive stereoscopic 3D video exposure-based treatment would be effective in reducing fear of spiders. Participants with a fear of spiders (N = 77) watched two psychoeducational videos with facts about spiders and phobias. They were then randomized to a treatment condition that watched a single session of a stereoscopic 3D immersive video exposure-based treatment (six 5-minute exposures) delivered through a virtual reality headset or a psychoeducation only control condition that watched a 30-minute neutral video (2D documentary) presented on a computer monitor. Assessments of spider fear (Fear of Spiders Questionnaire [FSQ], Behavioral Approach Task [BAT], & subjective ratings of fear) were completed pre- and post-treatment. Consistent with prediction, the stereoscopic 3D video condition outperformed the control condition in reducing fear of spiders showing a large between-group change effect size on the FSQ (Cohen’s d = 0.85) and a medium between-group effect size on the BAT (Cohen’s d = 0.47). This provides initial support for stereoscopic 3D video in treating phobias.

Manifold-defect depth-map restoration for very low-cost S3D videos

In this paper, the proposed algorithm provides a fluent and efficient method for repairing very-low quality depth maps of considerable manifold defects for low-cost stereoscopic 3D (S3D) photographing that such a depth map can be easily yielded by 1st-generation Kinect (Kinect-v1). The corresponding framework cascades two repairing portions named discriminative non-segmentation hole filling and edge rectification-by-deforming. The former can discriminatively fill a variety of depth-invalid holes with no need of practically making attribute-discrimination and target segmentation for depth holes. The main portions of the latter contain edge-shifting-rectification and texture-edge guided dual processing for tailoring possible twisted depth edges. Since the ingredients of proposed algorithm are compactly concatenated according to intimate context, most troublesome defects in Kinect-v1 depths can be tackled. Particularly, the proposed algorithm can obtain the restoring coherency for successive depth maps. A series of experimental results under various photographing scenarios can demonstrate that a single Kinect-v1 device can fast tell on the development of very low-cost S3D imaging tool by the proposed algorithm.