Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions

A full-reference stereoscopic image quality metric based on binocular energy and regression analysis

The recent developments of 3D media technology have brought to life numerous applications of interactive entertainment such as 3D cinema, 3DTV and gaming. However, due to the data intensive nature of 3D visual content, a number of research challenges have emerged. In order to optimise the end-to-end content life-cycle, from capture to processing and delivery, Quality of Experience (QoE) has become a major driving factor. This paper presents a human-centric approach to quality estimation of 3D visual content. A full reference quality assessment method for stereoscopic images is proposed. It is based on a Human Visual System (HVS) model to estimate subjective scores of registered stereoscopic images subjected to compression losses. The model has been trained with four publicly available registered stereoscopic image databases and a fixed relationship between subjective scores and the model has been determined. The high correlation of the relationship over a large number of stimuli has proven its consistency over the state-of-the-art

Adaptive subframe allocation for next generation multimedia delivery over hybrid LTE unicast broadcast

The continued global roll-out of long term evolution (LTE) networks is providing mobile users with perpetually increasing ubiquitous access to a rich selection of high quality multimedia. Interactive viewing experiences including 3-D or free-viewpoint video require the synchronous delivery of multiple video streams. This paper presents a novel hybrid unicast broadcast synchronisation (HUBS) framework to synchronously deliver multi-stream content. Previous techniques on hybrid LTE implementations include staggered modulation and coding scheme grouping, adaptive modulation coding or implementing error recover techniques; the work presented here instead focuses on dynamic allocation of resources between unicast and broadcast, improving stream synchronisation as well as overall cell resource usage. Furthermore, the HUBS framework has been developed to work within the limitations imposed by the LTE specification. Performance evaluation of the framework is performed through the simulation of probable future scenarios, where a popular live event is broadcast with stereo 3-D or multi-angle companion views interactively offered to capable users. The proposed framework forms a ``HUBS group'' that monitors the radio bearer queues to establish a time lead or lag between broadcast and unicast streams. Since unicast and broadcast share the same radio resources, the number of subframes allocated to the broadcast transmission are then dynamically increased or decreased to minimise the average lead/lag time offset between the streams. Dynamic allocation showed improvements for all services across the cell, whilst keeping streams synchronised despite increased user loading

Stereoscopic video quality assessment using binocular energy

Stereoscopic imaging is becoming increasingly popular. However, to ensure the best quality of experience, there is a need to develop more robust and accurate objective metrics for stereoscopic content quality assessment. Existing stereoscopic image and video metrics are either extensions of conventional 2D metrics (with added depth or disparity information) or are based on relatively simple perceptual models. Consequently, they tend to lack the accuracy and robustness required for stereoscopic content quality assessment. This paper introduces full-reference stereoscopic image and video quality metrics based on a Human Visual System (HVS) model incorporating important physiological findings on binocular vision. The proposed approach is based on the following three contributions. First, it introduces a novel HVS model extending previous models to include the phenomena of binocular suppression and recurrent excitation. Second, an image quality metric based on the novel HVS model is proposed. Finally, an optimised temporal pooling strategy is introduced to extend the metric to the video domain. Both image and video quality metrics are obtained via a training procedure to establish a relationship between subjective scores and objective measures of the HVS model. The metrics are evaluated using publicly available stereoscopic image/video databases as well as a new stereoscopic video database. An extensive experimental evaluation demonstrates the robustness of the proposed quality metrics. This indicates a considerable improvement with respect to the state-of-the-art with average correlations with subjective scores of 0.86 for the proposed stereoscopic image metric and 0.89 and 0.91 for the proposed stereoscopic video metrics