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Am 5. Juni 2024 findet an der Universität Klagenfurt die Antrittsvorlesung von Christian Timmerer vom Institut für Informationstechnologie zum Thema „Video Streaming: Then, Now, and in the Future“ statt.   

Das Rektorat der Universität Klagenfurt und der Dekan der Fakultät für Technische Wissenschaften laden herzlich am 5. Juni 2024 zur Antrittsvorlesung von Christian Timmerer ein. Christian Timmerer ist seit Dezember 2022 Universitätsprofessor für Multimedia Systems am Institut für Informationstechnologie der Fakultät für Technische Wissenschaften. Seine Antrittsvorlesung hält er zum Thema:

„Video Streaming: Then, Now, and in the Future“   [Slides]

5. Juni 2024
17.00 Uhr
Universität Klagenfurt
Hörsaal 2 (Zentraltrakt der Universität)

In seinem öffentlichen Vortrag gibt Christian Timmerer Einblicke in die faszinierende Geschichte des Videostreamings, beginnend bei den bescheidenen Anfängen vor YouTube bis hin zu den bahnbrechenden Technologien, die heutzutage Plattformen wie Netflix und ORF ON dominieren. Timmerer präsentiert dabei auch provokante eigene Beiträge, die die Branche maßgeblich beeinflusst haben. Zum Abschluss wirft er einen Blick auf die zukünftigen Herausforderungen und lädt das Publikum zu einer Diskussion ein.

Die Antrittsvorlesung findet in englischer Sprache statt.

European Signal Processing Conference (EUSIPCO)

26-30 August 2024, Lyon, France

[PDF]

Mohammad Ghasempour (AAU, Austria), Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria),  and Christian Timmerer (AAU, Austria)

Abstract: Video coding relies heavily on reducing spatial and temporal redundancy to enable efficient transmission. To tackle the temporal redundancy, each video frame is predicted from the previously encoded frames, known as reference frames. The quality of this prediction is highly dependent on the quality of the reference frames. Recent advancements in machine learning are motivating the exploration of frame synthesis to generate high-quality reference frames. However, the efficacy of such models depends on training with content similar to that encountered during usage, which is challenging due to the diverse nature of video data. This paper introduces a content-aware reference frame synthesis to enhance inter-prediction efficiency. Unlike conventional approaches that rely on pre-trained models, our proposed framework optimizes a deep learning model for each content by fine-tuning only the last layer of the model, requiring the transmission of only a few kilobytes of additional information to the decoder. Experimental results show that the proposed framework yields significant bitrate savings of 12.76%, outperforming its counterpart in the pre-trained framework, which only achieves 5.13% savings in bitrate.

Video Quality Experts Group (VQEG)

1-5 July 2024

University of Klagenfurt, Austria

[website]

The University of Klagenfurt welcomes VQEG members to the next meeting that will be held in Klagenfurt, Carinthia, Austria, from July 01-05, 2024.

The Video Quality Experts Group (VQEG) convenes a consortium of global specialists from various sectors, including industry, academia, governmental bodies, the International Telecommunication Union, and other standard-setting organizations.

Adaptive Bitrate Algorithm Deployed at Edge Nodes

US Patent

[URL][PDF]

Jesús Aguilar-Armijo (Alpen-Adria-Universität Klagenfurt, Austria), Ekrem Çetinkaya (Alpen-Adria-Universität Klagenfurt, Austria), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: The technology described herein relates to implementing an adaptive bitrate (ABR) algorithm at edge nodes. A method for implementing an ABR algorithm at an edge node may include receiving at the edge node a request for a video segment from a client according to the client’s ABR algorithm, the request indicating a quality. A weighted sum score for each of a set of qualities may be computed based on a quality score and a fairness score using the ABR algorithm at the edge node, the qualities including at least the requested quality and another quality. A modified request may be generated in response to the weighted sum score for the other quality being better than the weighted sum score for the requested quality. The modified request may be sent to a server. The video segment in the other quality may be received from the server and provided to a client.

IEEE Internet of Things Journal (IEEE IoT)

[PDF]

Hongjie He (Southwest Jiaotong University, China), Yuan Yuan (Southwest Jiaotong University, China), Hadi Amirpour (AAU, Klagenfurt, Austria),  Lingfeng Qu (Southwest Jiaotong University, China), Christian Timmerer (AAU, Klagenfurt, Austria), Fan Chen (Southwest Jiaotong University, China)

Abstract: With the development of Internet of Things (IoT) and cloud services, many images generated from IoT devices are stored in the cloud, calling for efficient data encryption methods. To balance the security and usability, the thumbnail preserving encryption (TPE) has emerged. However, existing JPEG image-based TPE (JPEG-TPE) schemes face challenges in achieving low file extension, lossless decryption and better privacy protect of detailed information. To solve these challenges, we propose a novel JPEG-TPE scheme.
Firstly, to achieve a smaller file size expansion and preserve the thumbnail, we reallocate the values, maintaining the sum for the DC difference instead of the DC coefficient. To ensure that the coefficients do not overflow, the valid range of reallocated difference is constrained not only by the sum but also by the neighborhood difference.
Secondly, to preserve file size of AC encryption while improve the security of detailed information, the AC coefficient groups with undivided RSV are permuted adaptively.
Besides, the intra TPE block swapping of DC difference, quantization table modification,
non-zero AC coefficients mapping, and block permutation are used to further encrypt the image. The experimental results show that the proposed JPEG-TPE scheme achieves lossless decryption, reducing the file size expansion of encrypted images from 15.41% to 0.64% compared to the state-of-the-art scheme. Additionally, it is observed that the proposed method can effectively resist against various attacks, including the deep-learning based super-resolution attack.