Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Perception and Mitigation of Artifacts in a Flat Panel Tiled Display System

Flat panel displays continue to dominate the display market. Larger, higher resolution flat panel displays are now in demand for scientific, business, and entertainment purposes. Manufacturing such large displays is currently difficult and expensive. Alternately, larger displays can be constructed by tiling smaller flat panel displays. While this approach may prove to be more cost effective, appropriate measures must be taken to achieve visual seamlessness and uniformity. In this project we conducted a set of experiments to study the perception and mitigation of image artifacts in tiled display systems. In the first experiment we used a prototype tiled display to investigate its current viability and to understand what critical perceptible visual artifacts exist in this system. Based on word frequencies of the survey responses, the most disruptive artifacts perceived were ranked. On the basis of these findings, we conducted a second experiment to test the effectiveness of image processing algorithms designed to mitigate some of the most distracting artifacts without changing the physical properties of the display system. Still images were processed using several algorithms and evaluated by observers using magnitude scaling. Participants in the experiment noticed statistically significant improvement in image quality from one of the two algorithms. Similar testing should be conducted to evaluate the effectiveness of the algorithms on video content. While much work still needs to be done, the contributions of this project should enable the development of an image processing pipeline to mitigate perceived artifacts in flat panel display systems and provide the groundwork for extending such a pipeline to realtime applications

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Understanding user interactivity for the next-generation immersive communication: design, optimisation, and behavioural analysis

Recent technological advances have opened the gate to a novel way to communicate remotely still feeling connected. In these immersive communications, humans are at the centre of virtual or augmented reality with a full sense of immersion and the possibility to interact with the new environment as well as other humans virtually present. These next-generation communication systems hide a huge potential that can invest in major economic sectors. However, they also posed many new technical challenges, mainly due to the new role of the final user: from merely passive to fully active in requesting and interacting with the content. Thus, we need to go beyond the traditional quality of experience research and develop user-centric solutions, in which the whole multimedia experience is tailored to the final interactive user. With this goal in mind, a better understanding of how people interact with immersive content is needed and it is the focus of this thesis. In this thesis, we study the behaviour of interactive users in immersive experiences and its impact on the next-generation multimedia systems. The thesis covers a deep literature review on immersive services and user centric solutions, before develop- ing three main research strands. First, we implement novel tools for behavioural analysis of users navigating in a 3-DoF Virtual Reality (VR) system. In detail, we study behavioural similarities among users by proposing a novel clustering algorithm. We also introduce information-theoretic metrics for quantifying similarities for the same viewer across contents. As second direction, we show the impact and advantages of taking into account user behaviour in immersive systems. Specifically, we formulate optimal user centric solutions i) from a server-side perspective and ii) a navigation aware adaptation logic for VR streaming platforms. We conclude by exploiting the aforementioned behavioural studies towards a more in- interactive immersive technology: a 6-DoF VR. Overall in this thesis, experimental results based on real navigation trajectories show key advantages of understanding any hidden patterns of user interactivity to be eventually exploited in engineering user centric solutions for immersive systems

Implementation of a distributed real-time video panorama pipeline for creating high quality virtual views

Today, we are continuously looking for more immersive video systems. Such systems, however, require more content, which can be costly to produce. A full panorama, covering regions of interest, can contain all the information required, but can be difficult to view in its entirety. In this thesis, we discuss a method for creating virtual views from a cylindrical panorama, allowing multiple users to create individual virtual cameras from the same panorama video. We discuss how this method can be used for video delivery, but emphasize on the creation of the initial panorama. The panorama must be created in real-time, and with very high quality. We design and implement a prototype recording pipeline, installed at a soccer stadium, as a part of the Bagadus project. We describe a pipeline capable of producing 4K panorama videos from five HD cameras, in real-time, with possibilities for further upscaling. We explain how the cylindrical panorama can be created, with minimal computational cost and without visible seams. The cameras of our prototype system record video in the incomplete Bayer format, and we also investigate which debayering algorithms are best suited for recording multiple high resolution video streams in real-time