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

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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)

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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.

IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

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 Hadi Amirpour (AAU, Klagenfurt, Austria), Klaus Schoeffmann (AAU, Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), Christian Timmerer (AAU, Klagenfurt, Austria)

Abstract: Video streaming and its applications are growing rapidly, making video optimization a primary target for content providers looking to enhance their services. Enhancing the quality of videos requires the adjustment of different encoding parameters such as bitrate, resolution, and frame rate. To avoid brute force approaches for predicting optimal encoding parameters, video complexity features are typically extracted and utilized. To predict optimal encoding parameters effectively, content providers traditionally use unsupervised feature extraction methods, such as ITU-T’s Spatial Information ( SI ) and Temporal Information ( TI ) to represent the spatial and temporal complexity of video sequences. Recently, Video Complexity Analyzer (VCA) was introduced to extract DCT-based features to represent the complexity of a video sequence (or parts thereof). These unsupervised features, however, cannot accurately predict video encoding parameters. To address this issue, this paper introduces a novel supervised feature extraction method named DeepVCA, which extracts the spatial and temporal complexity of video sequences using deep neural networks. In this approach, the encoding bits required to encode each frame in intra-mode and inter-mode are used as labels for spatial and temporal complexity, respectively. Initially, we benchmark various deep neural network structures to predict spatial complexity. We then leverage the similarity of features used to predict the spatial complexity of the current frame and its previous frame to rapidly predict temporal complexity. This approach is particularly useful as the temporal complexity may depend not only on the differences between two consecutive frames but also on their spatial complexity. Our proposed approach demonstrates significant improvement over unsupervised methods, especially for temporal complexity. As an example application, we verify the effectiveness of these features in predicting the encoding bitrate and encoding time of video sequences, which are crucial tasks in video streaming. The source code and dataset are available at https://github.com/cd-athena/ DeepVCA.

Variable Framerate Encoding using Content-aware Framerate Prediction for High Framerate Videos

US Patent

[PDF]

Vignesh Menon (Alpen-Adria-Universität Klagenfurt, Austria), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: The technology described herein relates to variable framerate encoding. A method for variable framerate encoding includes receiving shots, as segmented from a video input, extracting features for each of the shots, the features including at least a spatial energy feature and an average temporal energy, predicting a frame dropping factor for each of the shots based on the spatial energy feature and the average temporal energy, predicting an optimized framerate for each of the shots based on the frame dropping factor, downscaling and encoding each of the shots using the optimized framerate. The encoded shots may then be decoded and upscaled back to their original framerates.