Motion hints based video coding

The persistent growth of video-based applications is heavily dependent on the advancements in video coding systems. Modern video codecs use the motion model itself to describe the geometric boundaries of moving objects in video sequences and thereby spend a significant portion of their bit rate refining the motion description in regions where motion discontinuities exist. This explicit communication of motion introduces redundancy, since some aspects of the motion can at least partially be inferred from the reference frames. In this thesis work, a novel bi-directional motion hints based prediction paradigm is proposed that moves away from the traditional redundant approach of careful partitioning around object boundaries by exploiting the spatial structure of the reference frames to infer appropriate boundaries for the intermediate ones. Motion hint provide a global description of motion over specific domain. Fundamentally this is related to the segmentation of foreground from background regions where the foreground and background motions are the motion hints. The appealing thing about motion hints is that they are continuous and invertible, even though the observed motion field for a frame is discontinuous and non-invertible. Experimental results show that at low bit rate applications, the motion hints based coder achieved a rate-distortion (RD) gain of 0.81 dB, or equivalently 13.38% savings in bit rate over the H.264/AVC reference. In a hybrid setting, this gain increased to 0.94 dB and 20.41% bit rebate is obtained. If both low and high bit rate scenarios are considered then the hybrid coder showed a RD performance of 0.80 dB, or equivalently 16.57% savings in bit rate. The usage of higher fractional pixel accurate motion hint, predictive coding of motion hint, a memory-based initialization for motion hint estimation improved the RD gain to 0.85 dB and 17.55% of bit rebate. The prediction framework is highly flexible in the sense that the motion model order for the hints can be content adaptive i.e. it can accommodate different motion models like affine, elastic, etc. Detecting motion discontinuity macroblocks (MBs) is a challenging task and the prediction paradigm managed to detect a significant number of such MBs. If the motion hints based prediction is used as a prediction mode for MBs, at low bit rates almost 50% of the motion discontinuity MBs chose to use affine hint mode and this number increased to 60% if elastic hint is used

A coarse representation of frames oriented video coding by leveraging cuboidal partitioning of image data

Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. In this work, we form a coarse representation of the current frame by minimizing commonality within that frame while preserving important structural properties of the frame. The building blocks of this coarse representation are rectangular regions called cuboids, which are computationally simple and has a compact description. Then we propose to employ the coarse frame as an additional source for predictive coding of the current frame. Experimental results show an improvement in bit rate savings over a reference codec for HEVC, with minor increase in the codec computational complexity. © 2020 IEEE

Content-Aware Multimedia Communications

The demands for fast, economic and reliable dissemination of multimedia information are steadily growing within our society. While people and economy increasingly rely on communication technologies, engineers still struggle with their growing complexity. Complexity in multimedia communication originates from several sources. The most prominent is the unreliability of packet networks like the Internet. Recent advances in scheduling and error control mechanisms for streaming protocols have shown that the quality and robustness of multimedia delivery can be improved significantly when protocols are aware of the content they deliver. However, the proposed mechanisms require close cooperation between transport systems and application layers which increases the overall system complexity. Current approaches also require expensive metrics and focus on special encoding formats only. A general and efficient model is missing so far. This thesis presents efficient and format-independent solutions to support cross-layer coordination in system architectures. In particular, the first contribution of this work is a generic dependency model that enables transport layers to access content-specific properties of media streams, such as dependencies between data units and their importance. The second contribution is the design of a programming model for streaming communication and its implementation as a middleware architecture. The programming model hides the complexity of protocol stacks behind simple programming abstractions, but exposes cross-layer control and monitoring options to application programmers. For example, our interfaces allow programmers to choose appropriate failure semantics at design time while they can refine error protection and visibility of low-level errors at run-time. Based on some examples we show how our middleware simplifies the integration of stream-based communication into large-scale application architectures. An important result of this work is that despite cross-layer cooperation, neither application nor transport protocol designers experience an increase in complexity. Application programmers can even reuse existing streaming protocols which effectively increases system robustness.Der Bedarf unsere Gesellschaft nach kostengünstiger und zuverlässiger Kommunikation wächst stetig. Während wir uns selbst immer mehr von modernen Kommunikationstechnologien abhängig machen, müssen die Ingenieure dieser Technologien sowohl den Bedarf nach schneller Einführung neuer Produkte befriedigen als auch die wachsende Komplexität der Systeme beherrschen. Gerade die Übertragung multimedialer Inhalte wie Video und Audiodaten ist nicht trivial. Einer der prominentesten Gründe dafür ist die Unzuverlässigkeit heutiger Netzwerke, wie z.B.~dem Internet. Paketverluste und schwankende Laufzeiten können die Darstellungsqualität massiv beeinträchtigen. Wie jüngste Entwicklungen im Bereich der Streaming-Protokolle zeigen, sind jedoch Qualität und Robustheit der Übertragung effizient kontrollierbar, wenn Streamingprotokolle Informationen über den Inhalt der transportierten Daten ausnutzen. Existierende Ansätze, die den Inhalt von Multimediadatenströmen beschreiben, sind allerdings meist auf einzelne Kompressionsverfahren spezialisiert und verwenden berechnungsintensive Metriken. Das reduziert ihren praktischen Nutzen deutlich. Außerdem erfordert der Informationsaustausch eine enge Kooperation zwischen Applikationen und Transportschichten. Da allerdings die Schnittstellen aktueller Systemarchitekturen nicht darauf vorbereitet sind, müssen entweder die Schnittstellen erweitert oder alternative Architekturkonzepte geschaffen werden. Die Gefahr beider Varianten ist jedoch, dass sich die Komplexität eines Systems dadurch weiter erhöhen kann. Das zentrale Ziel dieser Dissertation ist es deshalb, schichtenübergreifende Koordination bei gleichzeitiger Reduzierung der Komplexität zu erreichen. Hier leistet die Arbeit zwei Beträge zum aktuellen Stand der Forschung. Erstens definiert sie ein universelles Modell zur Beschreibung von Inhaltsattributen, wie Wichtigkeiten und Abhängigkeitsbeziehungen innerhalb eines Datenstroms. Transportschichten können dieses Wissen zur effizienten Fehlerkontrolle verwenden. Zweitens beschreibt die Arbeit das Noja Programmiermodell für multimediale Middleware. Noja definiert Abstraktionen zur Übertragung und Kontrolle multimedialer Ströme, die die Koordination von Streamingprotokollen mit Applikationen ermöglichen. Zum Beispiel können Programmierer geeignete Fehlersemantiken und Kommunikationstopologien auswählen und den konkreten Fehlerschutz dann zur Laufzeit verfeinern und kontrolliere

WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting

Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred through annotating the original scenes with perception systems' predictions. Such intermediate representations tie the quality of the motion forecasting models to the performance of computer vision models. Moreover, the human-designed explicit interfaces between perception and motion forecasting typically pass only a subset of the semantic information present in the original sensory input. To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task. The new augmented dataset WOMD-LiDAR consists of over 100,000 scenes that each spans 20 seconds, consisting of well-synchronized and calibrated high quality LiDAR point clouds captured across a range of urban and suburban geographies (https://waymo.com/open/data/motion/). Compared to Waymo Open Dataset (WOD), WOMD-LiDAR dataset contains 100x more scenes. Furthermore, we integrate the LiDAR data into the motion forecasting model training and provide a strong baseline. Experiments show that the LiDAR data brings improvement in the motion forecasting task. We hope that WOMD-LiDAR will provide new opportunities for boosting end-to-end motion forecasting models.Comment: Dataset website: https://waymo.com/open/data/motion

A P2P Platform for real-time multicast video streaming leveraging on scalable multiple descriptions to cope with bandwidth fluctuations

In the immediate future video distribution applications will increase their diffusion thanks tothe ever-increasing user capabilities and improvements in the Internet access speed and performance.The target of this paper is to propose a content delivery system for real-time streaming services based ona peer-to-peer approach that exploits multicast overlay organization of the peers to address thechallenges due to bandwidth heterogeneity. To improve reliability and flexibility, video is coded using ascalable multiple description approach that allows delivery of sub-streams over multiple trees andallows rate adaptation along the trees as the available bandwidth changes. Moreover, we have deployeda new algorithm for tree-based topology management of the overlay network. In fact, tree based overlaynetworks better perform in terms of end-to-end delay and ordered delivery of video flow packets withrespect to mesh based ones. We also show with a case study that the proposed system works better thansimilar systems using only either multicast or multiple trees