Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory

Objective quality assessment of stereoscopic omnidirectional images is a challenging problem since it is influenced by multiple aspects such as projection deformation, field of view (FoV) range, binocular vision, visual comfort, etc. Existing studies show that classic 2D or 3D image quality assessment (IQA) metrics are not able to perform well for stereoscopic omnidirectional images. However, very few research works have focused on evaluating the perceptual visual quality of omnidirectional images, especially for stereoscopic omnidirectional images. In this paper, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images. Two modules are involved in SOIQE: predictive coding theory based binocular rivalry module and multi-view fusion module. In the binocular rivalry module, we introduce predictive coding theory to simulate the competition between high-level patterns and calculate the similarity and rivalry dominance to obtain the quality scores of viewport images. Moreover, we develop the multi-view fusion module to aggregate the quality scores of viewport images with the help of both content weight and location weight. The proposed SOIQE is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance. Experimental results on our published stereoscopic omnidirectional image quality assessment database (SOLID) demonstrate that our proposed SOIQE method outperforms state-of-the-art metrics. Furthermore, we also verify the effectiveness of each proposed module on both public stereoscopic image datasets and panoramic image datasets

An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to ef- fectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The highly interactive and flexible environment can capture and sustain more student attention than the traditional classroom setting and, thus, delivers a compelling experience to the students. Our main focus here is to investigate possible solutions for the system design and implementation and devise strategies for fast, efficient computation suitable for visual data processing and network transmission. We describe the technique and experiments in details and provide quantitative performance results, demonstrating our system can be run comfortably and reliably for different application scenarios. Our preliminary results are promising and demonstrate the potential for more compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

On the design of an ECOC-compliant genetic algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches

Three-dimensional media for mobile devices

Cataloged from PDF version of article.This paper aims at providing an overview of the core technologies enabling the delivery of 3-D Media to next-generation mobile devices. To succeed in the design of the corresponding system, a profound knowledge about the human visual system and the visual cues that form the perception of depth, combined with understanding of the user requirements for designing user experience for mobile 3-D media, are required. These aspects are addressed first and related with the critical parts of the generic system within a novel user-centered research framework. Next-generation mobile devices are characterized through their portable 3-D displays, as those are considered critical for enabling a genuine 3-D experience on mobiles. Quality of 3-D content is emphasized as the most important factor for the adoption of the new technology. Quality is characterized through the most typical, 3-D-specific visual artifacts on portable 3-D displays and through subjective tests addressing the acceptance and satisfaction of different 3-D video representation, coding, and transmission methods. An emphasis is put on 3-D video broadcast over digital video broadcasting-handheld (DVB-H) in order to illustrate the importance of the joint source-channel optimization of 3-D video for its efficient compression and robust transmission over error-prone channels. The comparative results obtained identify the best coding and transmission approaches and enlighten the interaction between video quality and depth perception along with the influence of the context of media use. Finally, the paper speculates on the role and place of 3-D multimedia mobile devices in the future internet continuum involving the users in cocreation and refining of rich 3-D media content

Light field image compression

Light field imaging based on a single-tier camera equipped with a micro-lens array has currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light field imaging applications and services will require identifying adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, this chapter presents some of the most recent light field image coding solutions that have been investigated. After a brief review of the current state of the art in image coding formats for light field photography, an experimental study of the rate-distortion performance for different coding formats and architectures is presented. Then, aiming at enabling faster deployment of light field applications and services in the consumer market, a scalable light field coding solution that provides backward compatibility with legacy display devices (e.g., 2D, 3D stereo, and 3D multiview) is also presented. Furthermore, a light field coding scheme based on a sparse set of microimages and the associated blockwise disparity is also presented. This coding scheme is scalable with three layers such that the rendering can be performed with the sparse micro-image set, the reconstructed light field image, and the decoded light field image.info:eu-repo/semantics/acceptedVersio