Supporting Arbitrary Zoom in Zoomable Video

Master'sMASTER OF SCIENC

ADAPTIVE WIRELESS NETWORKING FOR VIDEO STREAMING

Ph.DDOCTOR OF PHILOSOPH

A QoE adaptive management system for high definition video streaming over wireless networks

[EN] The development of the smart devices had led to demanding high-quality streaming videos over wireless communications. In Multimedia technology, the Ultra-High Definition (UHD) video quality has an important role due to the smart devices that are capable of capturing and processing high-quality video content. Since delivery of the high-quality video stream over the wireless networks adds challenges to the end-users, the network behaviors 'factors such as delay of arriving packets, delay variation between packets, and packet loss, are impacted on the Quality of Experience (QoE). Moreover, the characteristics of the video and the devices are other impacts, which influenced by the QoE. In this research work, the influence of the involved parameters is studied based on characteristics of the video, wireless channel capacity, and receivers' aspects, which collapse the QoE. Then, the impact of the aforementioned parameters on both subjective and objective QoE is studied. A smart algorithm for video stream services is proposed to optimize assessing and managing the QoE of clients (end-users). The proposed algorithm includes two approaches: first, using the machine-learning model to predict QoE. Second, according to the QoE prediction, the algorithm manages the video quality of the end-users by offering better video quality. As a result, the proposed algorithm which based on the least absolute shrinkage and selection operator (LASSO) regression is outperformed previously proposed methods for predicting and managing QoE of streaming video over wireless networks.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" with in the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja).Taha, M.; Canovas, A.; Lloret, J.; Ali, A. (2021). A QoE adaptive management system for high definition video streaming over wireless networks. Telecommunication Systems. 77(1):63-81. https://doi.org/10.1007/s11235-020-00741-2638177

Video processing for panoramic streaming using HEVC and its scalable extensions

Panoramic streaming is a particular way of video streaming where an arbitrary Region-of-Interest (RoI) is transmitted from a high-spatial resolution video, i.e. a video covering a very “wide-angle” (much larger than the human field-of-view – e.g. 360°). Some transport schemes for panoramic video delivery have been proposed and demonstrated within the past decade, which allow users to navigate interactively within the high-resolution videos. With the recent advances of head mounted displays, consumers may soon have immersive and sufficiently convenient end devices at reach, which could lead to an increasing demand for panoramic video experiences. The solution proposed within this paper is built upon tile-based panoramic streaming, where users receive a set of tiles that match their RoI, and consists in a low-complexity compressed domain video processing technique for using H.265/HEVC and its scalable extensions (H.265/SHVC and H.265/MV-HEVC). The proposed technique generates a single video bitstream out of the selected tiles so that a single hardware decoder can be used. It overcomes the scalability issue of previous solutions not using tiles and the battery consumption issue inherent of tile-based panorama streaming, where multiple parallel software decoders are used. In addition, the described technique is capable of reducing peak streaming bitrate during changes of the RoI, which is crucial for allowing a truly immersive and low latency video experience. Besides, it makes it possible to use Open GOP structures without incurring any playback interruption at switching events, which provides a better compression efficiency compared to closed GOP structures

Compréhension de contenus visuels par analyse conjointe du contenu et des usages

Dans cette thèse, nous traitons de la compréhension de contenus visuels, qu’il s’agisse d’images, de vidéos ou encore de contenus 3D. On entend par compréhension la capacité à inférer des informations sémantiques sur le contenu visuel. L’objectif de ce travail est d’étudier des méthodes combinant deux approches : 1) l’analyse automatique des contenus et 2) l’analyse des interactions liées à l’utilisation de ces contenus (analyse des usages, en plus bref). Dans un premier temps, nous étudions l’état de l’art issu des communautés de la vision par ordinateur et du multimédia. Il y a 20 ans, l’approche dominante visait une compréhension complètement automatique des images. Cette approche laisse aujourd’hui plus de place à différentes formes d’interventions humaines. Ces dernières peuvent se traduire par la constitution d’une base d’apprentissage annotée, par la résolution interactive de problèmes (par exemple de détection ou de segmentation) ou encore par la collecte d’informations implicites issues des usages du contenu. Il existe des liens riches et complexes entre supervision humaine d’algorithmes automatiques et adaptation des contributions humaines via la mise en œuvre d’algorithmes automatiques. Ces liens sont à l’origine de questions de recherche modernes : comment motiver des intervenants humains ? Comment concevoir des scénarii interactifs pour lesquels les interactions contribuent à comprendre le contenu manipulé ? Comment vérifier la qualité des traces collectées ? Comment agréger les données d’usage ? Comment fusionner les données d’usage avec celles, plus classiques, issues d’une analyse automatique ? Notre revue de la littérature aborde ces questions et permet de positionner les contributions de cette thèse. Celles-ci s’articulent en deux grandes parties. La première partie de nos travaux revisite la détection de régions importantes ou saillantes au travers de retours implicites d’utilisateurs qui visualisent ou acquièrent des con- tenus visuels. En 2D d’abord, plusieurs interfaces de vidéos interactives (en particulier la vidéo zoomable) sont conçues pour coordonner des analyses basées sur le contenu avec celles basées sur l’usage. On généralise ces résultats en 3D avec l’introduction d’un nouveau détecteur de régions saillantes déduit de la capture simultanée de vidéos de la même performance artistique publique (spectacles de danse, de chant etc.) par de nombreux utilisateurs. La seconde contribution de notre travail vise une compréhension sémantique d’images fixes. Nous exploitons les données récoltées à travers un jeu, Ask’nSeek, que nous avons créé. Les interactions élémentaires (comme les clics) et les données textuelles saisies par les joueurs sont, comme précédemment, rapprochées d’analyses automatiques des images. Nous montrons en particulier l’intérêt d’interactions révélatrices des relations spatiales entre différents objets détectables dans une même scène. Après la détection des objets d’intérêt dans une scène, nous abordons aussi le problème, plus ambitieux, de la segmentation. ABSTRACT : This thesis focuses on the problem of understanding visual contents, which can be images, videos or 3D contents. Understanding means that we aim at inferring semantic information about the visual content. The goal of our work is to study methods that combine two types of approaches: 1) automatic content analysis and 2) an analysis of how humans interact with the content (in other words, usage analysis). We start by reviewing the state of the art from both Computer Vision and Multimedia communities. Twenty years ago, the main approach was aiming at a fully automatic understanding of images. This approach today gives way to different forms of human intervention, whether it is through the constitution of annotated datasets, or by solving problems interactively (e.g. detection or segmentation), or by the implicit collection of information gathered from content usages. These different types of human intervention are at the heart of modern research questions: how to motivate human contributors? How to design interactive scenarii that will generate interactions that contribute to content understanding? How to check or ensure the quality of human contributions? How to aggregate human contributions? How to fuse inputs obtained from usage analysis with traditional outputs from content analysis? Our literature review addresses these questions and allows us to position the contributions of this thesis. In our first set of contributions we revisit the detection of important (or salient) regions through implicit feedback from users that either consume or produce visual contents. In 2D, we develop several interfaces of interactive video (e.g. zoomable video) in order to coordinate content analysis and usage analysis. We also generalize these results to 3D by introducing a new detector of salient regions that builds upon simultaneous video recordings of the same public artistic performance (dance show, chant, etc.) by multiple users. The second contribution of our work aims at a semantic understanding of fixed images. With this goal in mind, we use data gathered through a game, Ask’nSeek, that we created. Elementary interactions (such as clicks) together with textual input data from players are, as before, mixed with automatic analysis of images. In particular, we show the usefulness of interactions that help revealing spatial relations between different objects in a scene. After studying the problem of detecting objects on a scene, we also adress the more ambitious problem of segmentation

An Advanced A-V- Player to Support Scalable Personalised Interaction with Multi-Stream Video Content

PhDCurrent Audio-Video (A-V) players are limited to pausing, resuming, selecting and viewing a single video stream of a live broadcast event that is orchestrated by a professional director. The main objective of this research is to investigate how to create a new custom-built interactive A V player that enables viewers to personalise their own orchestrated views of live events from multiple simultaneous camera streams, via interacting with tracked moving objects, being able to zoom in and out of targeted objects, and being able to switch views based upon detected incidents in specific camera views. This involves research and development of a personalisation framework to create and maintain user profiles that are acquired implicitly and explicitly and modelling how this framework supports an evaluation of the effectiveness and usability of personalisation. Personalisation is considered from both an application oriented and a quality supervision oriented perspective within the proposed framework. Personalisation models can be individually or collaboratively linked with specific personalisation usage scenarios. The quality of different personalised interaction in terms of explicit evaluative metrics such as scalability and consistency can be monitored and measured using specific evaluation mechanisms.European Union's Seventh Framework Programme ([FP7/2007-2013]) under grant agreement No. ICT- 215248 and from Queen Mary University of London