Image and Video Coding Techniques for Ultra-low Latency

The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.publishedVersionPeer reviewe

Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

Bitstream-based video quality modeling and analysis of HTTP-based adaptive streaming

Die Verbreitung erschwinglicher Videoaufnahmetechnologie und verbesserte Internetbandbreiten ermöglichen das Streaming von hochwertigen Videos (Auflösungen > 1080p, Bildwiederholraten ≥ 60fps) online. HTTP-basiertes adaptives Streaming ist die bevorzugte Methode zum Streamen von Videos, bei der Videoparameter an die verfügbare Bandbreite angepasst wird, was sich auf die Videoqualität auswirkt. Adaptives Streaming reduziert Videowiedergabeunterbrechnungen aufgrund geringer Netzwerkbandbreite, wirken sich jedoch auf die wahrgenommene Qualität aus, weswegen eine systematische Bewertung dieser notwendig ist. Diese Bewertung erfolgt üblicherweise für kurze Abschnitte von wenige Sekunden und während einer Sitzung (bis zu mehreren Minuten). Diese Arbeit untersucht beide Aspekte mithilfe perzeptiver und instrumenteller Methoden. Die perzeptive Bewertung der kurzfristigen Videoqualität umfasst eine Reihe von Labortests, die in frei verfügbaren Datensätzen publiziert wurden. Die Qualität von längeren Sitzungen wurde in Labortests mit menschlichen Betrachtern bewertet, die reale Betrachtungsszenarien simulieren. Die Methodik wurde zusätzlich außerhalb des Labors für die Bewertung der kurzfristigen Videoqualität und der Gesamtqualität untersucht, um alternative Ansätze für die perzeptive Qualitätsbewertung zu erforschen. Die instrumentelle Qualitätsevaluierung wurde anhand von bitstrom- und hybriden pixelbasierten Videoqualitätsmodellen durchgeführt, die im Zuge dieser Arbeit entwickelt wurden. Dazu wurde die Modellreihe AVQBits entwickelt, die auf den Labortestergebnissen basieren. Es wurden vier verschiedene Modellvarianten von AVQBits mit verschiedenen Inputinformationen erstellt: Mode 3, Mode 1, Mode 0 und Hybrid Mode 0. Die Modellvarianten wurden untersucht und schneiden besser oder gleichwertig zu anderen aktuellen Modellen ab. Diese Modelle wurden auch auf 360°- und Gaming-Videos, HFR-Inhalte und Bilder angewendet. Darüber hinaus wird ein Langzeitintegrationsmodell (1 - 5 Minuten) auf der Grundlage des ITU-T-P.1203.3-Modells präsentiert, das die verschiedenen Varianten von AVQBits mit sekündigen Qualitätswerten als Videoqualitätskomponente des vorgeschlagenen Langzeitintegrationsmodells verwendet. Alle AVQBits-Varianten, das Langzeitintegrationsmodul und die perzeptiven Testdaten wurden frei zugänglich gemacht, um weitere Forschung zu ermöglichen.The pervasion of affordable capture technology and increased internet bandwidth allows high-quality videos (resolutions > 1080p, framerates ≥ 60fps) to be streamed online. HTTP-based adaptive streaming is the preferred method for streaming videos, adjusting video quality based on available bandwidth. Although adaptive streaming reduces the occurrences of video playout being stopped (called “stalling”) due to narrow network bandwidth, the automatic adaptation has an impact on the quality perceived by the user, which results in the need to systematically assess the perceived quality. Such an evaluation is usually done on a short-term (few seconds) and overall session basis (up to several minutes). In this thesis, both these aspects are assessed using subjective and instrumental methods. The subjective assessment of short-term video quality consists of a series of lab-based video quality tests that have resulted in publicly available datasets. The overall integral quality was subjectively assessed in lab tests with human viewers mimicking a real-life viewing scenario. In addition to the lab tests, the out-of-the-lab test method was investigated for both short-term video quality and overall session quality assessment to explore the possibility of alternative approaches for subjective quality assessment. The instrumental method of quality evaluation was addressed in terms of bitstream- and hybrid pixel-based video quality models developed as part of this thesis. For this, a family of models, namely AVQBits has been conceived using the results of the lab tests as ground truth. Based on the available input information, four different instances of AVQBits, that is, a Mode 3, a Mode 1, a Mode 0, and a Hybrid Mode 0 model are presented. The model instances have been evaluated and they perform better or on par with other state-of-the-art models. These models have further been applied to 360° and gaming videos, HFR content, and images. Also, a long-term integration (1 - 5 mins) model based on the ITU-T P.1203.3 model is presented. In this work, the different instances of AVQBits with the per-1-sec scores output are employed as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module and the subjective test data are made publicly available for further research

An objective and subjective quality assessment for passive gaming video streaming

Gaming video streaming has become increasingly popular in recent times. Along with the rise and popularity of cloud gaming services and e-sports, passive gaming video streaming services such as Twitch.tv, YouTubeGaming, etc. where viewers watch the gameplay of other gamers, have seen increasing acceptance. Twitch.tv alone has over 2.2 million monthly streamers and 15 million daily active users with almost a million average concurrent users, making Twitch.tv the 4th biggest internet traffic generator, just after Netflix, YouTube and Apple. Despite the increasing importance and popularity of such live gaming video streaming services, they have until recently not caught the attention of the quality assessment research community. For the continued success of such services, it is imperative to maintain and satisfy the end user Quality of Experience (QoE), which can be measured using various Video Quality Assessment (VQA) methods. Gaming videos are synthetic and artificial in nature and have different streaming requirements as compared to traditional non-gaming content. While there exist a lot of subjective and objective studies in the field of quality assessment of Video-on-demand (VOD) streaming services, such as Netflix and YouTube, along with the design of many VQA metrics, no work has been done previously towards quality assessment of live passive gaming video streaming applications. The research work in this thesis tries to address this gap by using various subjective and objective quality assessment studies. A codec comparison using the three most popular and widely used compression standards is performed to determine their compression efficiency. Furthermore, a subjective and objective comparative study is carried out to find out the difference between gaming and non-gaming videos in terms of the trade-off between quality and data-rate after compression. This is followed by the creation of an open source gaming video dataset, which is then used for a performance evaluation study of the eight most popular VQA metrics. Different temporal pooling strategies and content based classification approaches are evaluated to assess their effect on the VQA metrics. Finally, due to the low performance of existing No-Reference (NR) VQA metrics on gaming video content, two machine learning based NR models are designed using NR features and existing NR metrics, which are shown to outperform existing NR metrics while performing on par with state-of-the-art Full-Reference (FR) VQA metrics

A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption

Quality of Experience in Immersive Video Technologies

Over the last decades, several technological revolutions have impacted the television industry, such as the shifts from black & white to color and from standard to high-definition. Nevertheless, further considerable improvements can still be achieved to provide a better multimedia experience, for example with ultra-high-definition, high dynamic range & wide color gamut, or 3D. These so-called immersive technologies aim at providing better, more realistic, and emotionally stronger experiences. To measure quality of experience (QoE), subjective evaluation is the ultimate means since it relies on a pool of human subjects. However, reliable and meaningful results can only be obtained if experiments are properly designed and conducted following a strict methodology. In this thesis, we build a rigorous framework for subjective evaluation of new types of image and video content. We propose different procedures and analysis tools for measuring QoE in immersive technologies. As immersive technologies capture more information than conventional technologies, they have the ability to provide more details, enhanced depth perception, as well as better color, contrast, and brightness. To measure the impact of immersive technologies on the viewersâ QoE, we apply the proposed framework for designing experiments and analyzing collected subjectsâ ratings. We also analyze eye movements to study human visual attention during immersive content playback. Since immersive content carries more information than conventional content, efficient compression algorithms are needed for storage and transmission using existing infrastructures. To determine the required bandwidth for high-quality transmission of immersive content, we use the proposed framework to conduct meticulous evaluations of recent image and video codecs in the context of immersive technologies. Subjective evaluation is time consuming, expensive, and is not always feasible. Consequently, researchers have developed objective metrics to automatically predict quality. To measure the performance of objective metrics in assessing immersive content quality, we perform several in-depth benchmarks of state-of-the-art and commonly used objective metrics. For this aim, we use ground truth quality scores, which are collected under our subjective evaluation framework. To improve QoE, we propose different systems for stereoscopic and autostereoscopic 3D displays in particular. The proposed systems can help reducing the artifacts generated at the visualization stage, which impact picture quality, depth quality, and visual comfort. To demonstrate the effectiveness of these systems, we use the proposed framework to measure viewersâ preference between these systems and standard 2D & 3D modes. In summary, this thesis tackles the problems of measuring, predicting, and improving QoE in immersive technologies. To address these problems, we build a rigorous framework and we apply it through several in-depth investigations. We put essential concepts of multimedia QoE under this framework. These concepts not only are of fundamental nature, but also have shown their impact in very practical applications. In particular, the JPEG, MPEG, and VCEG standardization bodies have adopted these concepts to select technologies that were proposed for standardization and to validate the resulting standards in terms of compression efficiency

Data-driven visual quality estimation using machine learning

Heutzutage werden viele visuelle Inhalte erstellt und sind zugänglich, was auf Verbesserungen der Technologie wie Smartphones und das Internet zurückzuführen ist. Es ist daher notwendig, die von den Nutzern wahrgenommene Qualität zu bewerten, um das Erlebnis weiter zu verbessern. Allerdings sind nur wenige der aktuellen Qualitätsmodelle speziell für höhere Auflösungen konzipiert, sagen mehr als nur den Mean Opinion Score vorher oder nutzen maschinelles Lernen. Ein Ziel dieser Arbeit ist es, solche maschinellen Modelle für höhere Auflösungen mit verschiedenen Datensätzen zu trainieren und zu evaluieren. Als Erstes wird eine objektive Analyse der Bildqualität bei höheren Auflösungen durchgeführt. Die Bilder wurden mit Video-Encodern komprimiert, hierbei weist AV1 die beste Qualität und Kompression auf. Anschließend werden die Ergebnisse eines Crowd-Sourcing-Tests mit einem Labortest bezüglich Bildqualität verglichen. Weiterhin werden auf Deep Learning basierende Modelle für die Vorhersage von Bild- und Videoqualität beschrieben. Das auf Deep Learning basierende Modell ist aufgrund der benötigten Ressourcen für die Vorhersage der Videoqualität in der Praxis nicht anwendbar. Aus diesem Grund werden pixelbasierte Videoqualitätsmodelle vorgeschlagen und ausgewertet, die aussagekräftige Features verwenden, welche Bild- und Bewegungsaspekte abdecken. Diese Modelle können zur Vorhersage von Mean Opinion Scores für Videos oder sogar für anderer Werte im Zusammenhang mit der Videoqualität verwendet werden, wie z.B. einer Bewertungsverteilung. Die vorgestellte Modellarchitektur kann auf andere Videoprobleme angewandt werden, wie z.B. Videoklassifizierung, Vorhersage der Qualität von Spielevideos, Klassifikation von Spielegenres oder der Klassifikation von Kodierungsparametern. Ein wichtiger Aspekt ist auch die Verarbeitungszeit solcher Modelle. Daher wird ein allgemeiner Ansatz zur Beschleunigung von State-of-the-Art-Videoqualitätsmodellen vorgestellt, der zeigt, dass ein erheblicher Teil der Verarbeitungszeit eingespart werden kann, während eine ähnliche Vorhersagegenauigkeit erhalten bleibt. Die Modelle sind als Open Source veröffentlicht, so dass die entwickelten Frameworks für weitere Forschungsarbeiten genutzt werden können. Außerdem können die vorgestellten Ansätze als Bausteine für neuere Medienformate verwendet werden.Today a lot of visual content is accessible and produced, due to improvements in technology such as smartphones and the internet. This results in a need to assess the quality perceived by users to further improve the experience. However, only a few of the state-of-the-art quality models are specifically designed for higher resolutions, predict more than mean opinion score, or use machine learning. One goal of the thesis is to train and evaluate such machine learning models of higher resolutions with several datasets. At first, an objective evaluation of image quality in case of higher resolutions is performed. The images are compressed using video encoders, and it is shown that AV1 is best considering quality and compression. This evaluation is followed by the analysis of a crowdsourcing test in comparison with a lab test investigating image quality. Afterward, deep learning-based models for image quality prediction and an extension for video quality are proposed. However, the deep learning-based video quality model is not practically usable because of performance constrains. For this reason, pixel-based video quality models using well-motivated features covering image and motion aspects are proposed and evaluated. These models can be used to predict mean opinion scores for videos, or even to predict other video quality-related information, such as a rating distributions. The introduced model architecture can be applied to other video problems, such as video classification, gaming video quality prediction, gaming genre classification or encoding parameter estimation. Furthermore, one important aspect is the processing time of such models. Hence, a generic approach to speed up state-of-the-art video quality models is introduced, which shows that a significant amount of processing time can be saved, while achieving similar prediction accuracy. The models have been made publicly available as open source so that the developed frameworks can be used for further research. Moreover, the presented approaches may be usable as building blocks for newer media formats

Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation

There has been a dramatic increase in the amount of video traffic over the Internet in past several years. For applications like real-time video streaming and video conferencing, retransmission of lost packets is often not permitted. Popular video coding standards such as H.26x and VPx make use of spatial-temporal correlations for compression, typically making compressed bitstreams vulnerable to errors. We propose several adaptive spatial-temporal error concealment approaches for subsampling-based multiple description video coding. These adaptive methods are based on motion and mode information extracted from the H.26x video bitstreams. We also present an error resilience method using data duplication in VPx video bitstreams. A recent challenge in image processing is the analysis of biomedical images acquired using optical microscopy. Due to the size and complexity of the images, automated segmentation methods are required to obtain quantitative, objective and reproducible measurements of biological entities. In this thesis, we present two techniques for microscopy image analysis. Our first method, “Jelly Filling” is intended to provide 3D segmentation of biological images that contain incompleteness in dye labeling. Intuitively, this method is based on filling disjoint regions of an image with jelly-like fluids to iteratively refine segments that represent separable biological entities. Our second method selectively uses a shape-based function optimization approach and a 2D marked point process simulation, to quantify nuclei by their locations and sizes. Experimental results exhibit that our proposed methods are effective in addressing the aforementioned challenges

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

Receiver-Driven Video Adaptation

In the span of a single generation, video technology has made an incredible impact on daily life. Modern use cases for video are wildly diverse, including teleconferencing, live streaming, virtual reality, home entertainment, social networking, surveillance, body cameras, cloud gaming, and autonomous driving. As these applications continue to grow more sophisticated and heterogeneous, a single representation of video data can no longer satisfy all receivers. Instead, the initial encoding must be adapted to each receiver's unique needs. Existing adaptation strategies are fundamentally flawed, however, because they discard the video's initial representation and force the content to be re-encoded from scratch. This process is computationally expensive, does not scale well with the number of videos produced, and throws away important information embedded in the initial encoding. Therefore, a compelling need exists for the development of new strategies that can adapt video content without fully re-encoding it. To better support the unique needs of smart receivers, diverse displays, and advanced applications, general-use video systems should produce and offer receivers a more flexible compressed representation that supports top-down adaptation strategies from an original, compressed-domain ground truth. This dissertation proposes an alternate model for video adaptation that addresses these challenges. The key idea is to treat the initial compressed representation of a video as the ground truth, and allow receivers to drive adaptation by dynamically selecting which subsets of the captured data to receive. In support of this model, three strategies for top-down, receiver-driven adaptation are proposed. First, a novel, content-agnostic entropy coding technique is implemented in which symbols are selectively dropped from an input abstract symbol stream based on their estimated probability distributions to hit a target bit rate. Receivers are able to guide the symbol dropping process by supplying the encoder with an appropriate rate controller algorithm that fits their application needs and available bandwidths. Next, a domain-specific adaptation strategy is implemented for H.265/HEVC coded video in which the prediction data from the original source is reused directly in the adapted stream, but the residual data is recomputed as directed by the receiver. By tracking the changes made to the residual, the encoder can compensate for decoder drift to achieve near-optimal rate-distortion performance. Finally, a fully receiver-driven strategy is proposed in which the syntax elements of a pre-coded video are cataloged and exposed directly to clients through an HTTP API. Instead of requesting the entire stream at once, clients identify the exact syntax elements they wish to receive using a carefully designed query language. Although an implementation of this concept is not provided, an initial analysis shows that such a system could save bandwidth and computation when used by certain targeted applications.Doctor of Philosoph