Quality of experience in skype video calls

Skype is one of the well-known applications that has guided the evolution of real-time video streaming and has become one of the most used software in everyday life. It provides VoIP audio/video calls as well as messaging chat and file transfer. Many versions are available covering all the principal operating systems like Windows, Macintosh and Linux but also mobile systems. Voice quality decreed Skype success since its birth in 2003 and peer-to-peer architecture has allowed worldwide diffusion. After video call introduction in 2006 Skype became a complete solution to communicate between two or more people. As a primarily video conferencing application, Skype assumes certain characteristics of the delivered video to optimize its perceived quality. However in the last years, and with the recent release of SkypeKit1, many new Skype video-enabled devices came out especially in the mobile world. This forced a change to the traditional recording, streaming and receiving settings allowing for a wide range of network and content dynamics. Video calls are not anymore based on static ‘chatting’ but mobile devices have opened new possibilities and can be used in several scenarios. For instance, lecture streaming or one-to-one mobile video conferences exhibit more dynamics as both caller and callee might be on move. Most of these cases are different from “head&shoulder” only content. Therefore, Skype needs to optimize its video streaming engine to cover more video types. Heterogeneous connections require different behaviors and solutions and Skype must face with this variety to maintain a certain quality independently from connection used. Part of the present work will be focused on analyzing Skype behavior depending on video content. Since Skype protocol is proprietary most of the studies so far have tried to characterize its traffic and to reverse engineer its protocol. However, questions related to the behavior of Skype, especially on quality as perceived by users, remain unanswered. We will study Skype video codecs capabilities and video quality assessment. Another motivation of our work is the design of a mechanism that estimates the perceived cost of network conditions on Skype video delivery. To this extent we will try to assess in an objective way the impact of network impairments on the perceived quality of a Skype video call. Traditional video streaming schemes lack the necessary flexibility and adaptivity that Skype tries to achieve at the edge of a network. Our contribution will lye on a testbed and consequent objective video quality analysis that we will carry out on input videos. We will stream raw video files with Skype via an impaired channel and then we will record it at the receiver side to analyze with objective quality of experience metrics.

Deep learning models for the automatic analysis of volleyball plays

Collecting and analysing data is an important element in any field of human activity and research. Even in sports, collecting and analyzing statistical data is attracting a growing interest. Some exemplar use cases are: improvement of technical/tactical aspects for team coaches, definition of game strategies based on the opposite team play or evaluation of the performance of players. Other advantages are related to taking more precise and impartial judgment in referee decisions: a wrong decision can change the outcomes of important matches. Finally, it can be useful to provide better representations and graphic effects that make the game more engaging for the audience during the match. Nowadays it is possible to delegate this type of task to automatic software systems that can use cameras or even hardware sensors to collect images or data and process them. One of the most efficient methods to collect data is to process the video images of the sporting event through mixed techniques concerning machine learning applied to computer vision. As in other domains in which computer vision can be applied, the main tasks in sports are related to object detection, player tracking, and to the pose estimation of athletes. The goal of the present thesis is to apply different models of CNNs to analyze volleyball matches. Starting from video frames of a volleyball match, we reproduce a bird's eye view of the playing court where all the players are projected, reporting also for each player the type of action she/he is performing.