Hybrid Sequencing of Uncompressed and Compressed 3D Stereoscopic Video: A Preliminary Quality Evaluation

The use of 3D stereoscopic technology with high quality videos can provide visual entertainment to viewers. However, the bandwidth of typical communication channels cannot transmit uncompressed 3D videos, resulting in the need for video quality compression. This paper presents a series of preliminary studies to investigate the subjective perception of uncompressed and compressed video sequences, and proposes the ‘hybrid’ sequencing of uncompressed and compressed content in a single stereoscopic 3D video as an alternative approach for limited bandwidth transmission. However, the hybrid uncompressed/compressed sequencing of stereoscopic 3D video may affect the correlation between the left and right views of the stereoscopic videos required for depth perception, potentially leading to lower Quality of Experience (QoE) of viewers. This paper therefore investigates both the objective and subjective quality evaluation of the proposed hybrid sequencing of stereoscopic video sequences. Initial investigations into objective metrics to measure the difference in quality of the two stereoscopic views due to the proposed hybrid sequencing of uncompressed and compressed videos were also conducted

Video selection for visual sensor networks: A motion-based ranking algorithm

A Visual Sensor Network (VSN) is composed by several cameras, in general with different characteristics and orientations, which are used to cover a certain Area of Interest (AoI). To provide an optimal and autonomous exploitation of the VSN video streams, suitable algorithms are needed for selecting the cameras capable to guarantee the best video quality for the specific AoI in the scene. In this work, a novel content and context-aware camera ranking algorithm is proposed, with the goal to maximize the Quality of Experience (QoE) to the final user. The proposed algorithm takes into account the pose, camera resolution and frame rate, and the quantity of motion in the scene. Subjective tests are performed to compare the ranking of the algorithm with human ranking. Finally, the proposed ranking algorithm is compared with common objective video quality metrics and a previous ranking algorithm, confirming the validity of the approach

Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos that are both visually appealing and semantically meaningful. Given a scene with multiple objects, there are many cinemagraphs one could create. Our method evaluates these multiple candidates and presents the best one, as determined by a model trained to predict human preferences in a collaborative way. We demonstrate the effectiveness of our approach with multiple results and a user study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary materia

Subjective quality study of adaptive streaming of monoscopic and stereoscopic video

Nowadays, HTTP adaptive streaming (HAS) has become a reliable distribution technology offering significant advantages in terms of both user perceived Quality of Experience (QoE) and resource utilization for content and network service providers. By trading-off the video quality, HAS is able to adapt to the available bandwidth and display requirements so that it can deliver the video content to a variety of devices over the Internet. However, until now there is not enough knowledge of how the adaptation techniques affect the end user's visual experience. Therefore, this paper presents a comparative analysis of different bitrate adaptation strategies in adaptive streaming of monoscopic and stereoscopic video. This has been done through a subjective experiment of testing the end-user response to the video quality variations, considering the visual comfort issue. The experimental outcomes have made a good insight into the factors that can influence on the QoE of different adaptation strategies