Video streaming

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File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments

In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper

Digital rights management techniques for H.264 video

This work aims to present a number of low-complexity digital rights management (DRM) methodologies for the H.264 standard. Initially, requirements to enforce DRM are analyzed and understood. Based on these requirements, a framework is constructed which puts forth different possibilities that can be explored to satisfy the objective. To implement computationally efficient DRM methods, watermarking and content based copy detection are then chosen as the preferred methodologies. The first approach is based on robust watermarking which modifies the DC residuals of 4×4 macroblocks within I-frames. Robust watermarks are appropriate for content protection and proving ownership. Experimental results show that the technique exhibits encouraging rate-distortion (R-D) characteristics while at the same time being computationally efficient. The problem of content authentication is addressed with the help of two methodologies: irreversible and reversible watermarks. The first approach utilizes the highest frequency coefficient within 4×4 blocks of the I-frames after CAVLC en- tropy encoding to embed a watermark. The technique was found to be very effect- ive in detecting tampering. The second approach applies the difference expansion (DE) method on IPCM macroblocks within P-frames to embed a high-capacity reversible watermark. Experiments prove the technique to be not only fragile and reversible but also exhibiting minimal variation in its R-D characteristics. The final methodology adopted to enforce DRM for H.264 video is based on the concept of signature generation and matching. Specific types of macroblocks within each predefined region of an I-, B- and P-frame are counted at regular intervals in a video clip and an ordinal matrix is constructed based on their count. The matrix is considered to be the signature of that video clip and is matched with longer video sequences to detect copies within them. Simulation results show that the matching methodology is capable of not only detecting copies but also its location within a longer video sequence. Performance analysis depict acceptable false positive and false negative rates and encouraging receiver operating charac- teristics. Finally, the time taken to match and locate copies is significantly low which makes it ideal for use in broadcast and streaming applications

Watermarking for the Secure Transmission of the Key into an Encrypted Image

Ensuring the confidentiality of any data exchanged always presents a great concern for all communication instances. Technically, encryption is the ideal solution for this task. However, this process must deal with the progress of the cryptanalysis that aims to disclose the information exchanged. The risk increases due to the need for a dual transmission that includes the encrypted medium and the decryption key. In a context of chaotic encryption of images, we propose to insert the decryption key into the encrypted image using image watermarking. Thus, only the watermarked encrypted image will be transmitted. Upon reception, the recipient extracts the key and decrypts the image. The cryptosystem proposed is based on an encryption using a dynamic Look-Up Table issued from a chaotic generator. The obtained results prove the efficiency of our method to ensure a secure exchange of images and keys

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

MPAI-EEV: Standardization Efforts of Artificial Intelligence based End-to-End Video Coding

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric