IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, South Korea

http://www.vcip2023.org/

[PDF][Slides][Video]

Lingfeng Qu (SWJTU, China), Hongjie He (SWJTU, China), Hadi Amirpour (AAU, Austria), Mohammad Ghanbari (University of Essex, UK) , and Christian Timmerer (AAU, Austria)

Abstract: In this paper, we propose a novel attack model called the Got Plaintext Attack (GPA), where the attacker only requires one plaintext and the ciphertext image set stored in the cloud to attack the content of the ciphertext image. Using this model, we examine the security of the Improved Redundant Space Transfer (IRST) encryption method. To this end, we define an ordered characteristic matrix based on the properties of the three keys used in IRST. By comparing the histogram distance of the ordered characteristic matrix, we are able to obtain a plain-ciphertext pair. Furthermore, by leveraging the invariant properties of the ordered characteristic matrix of image blocks in the plain-ciphertext pair, we estimate the block permutation Π2 and the bit-plane permutation sequence Π1. Our experiments show that the accuracy of estimating Π2 is higher than 70% for block sizes of 3×3 pixels or larger. Despite a 40% accuracy in estimating Π1, the content information of the ciphertext image can still be exposed.

IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, Korea

http://www.vcip2023.org/

[PDF][Slides][Video]

Jingwen zhu (Nantes University), Hadi Amirpour (AAU, Austria), Raimund Schatz (AIT, Austria) ,  Christian Timmerer (AAU, Austria), and Patrick Le Callet (Nantes University)

Abstract: In adaptive video streaming, optimizing the selection of representations for the encoding bitrate ladder has a significant impact on the quality and economics of media delivery. An efficient way to select representations for the bitrate ladder of a given clip is to consider the Satisfied User Ratio (SUR) of the perceived quality of consecutive representations. This ensures that only representations with one Just Noticeable Difference (JND) are encoded and streamed by avoiding encoding similar-quality representation. VMAF (Video Multi-method Assessment Fusion) presently stands as the most commonly utilized quality metric for constructing bitrate ladders. Hence, the precise determination of JND-optimal encoding step-sizes for the VMAF proxy holds paramount importance; nevertheless, this task is intricate and can present considerable challenges. In this paper, we evaluate the effectiveness of different Video Quality Metrics (VQM) in predicting SUR for the VMAF proxy to better capture content-specific characteristics. Our experimental results provide evidence that incorporating VQM can improve the precision of the SUR prediction for the VMAF proxy. Compared to a state-of-the-art approach that utilizes video complexity metrics, our proposed approach, which incorporates two quality metrics—specifically, VMAF and SSIM calculated at an optimized quantization parameter (QP)—achieves a substantially reduced Mean Absolute Error (MAE) of 1.67. In contrast, the state-of-the-art approach yields an MAE of 2.01. Hence, we recommend using the above quality metrics to improve the accuracy of SUR prediction for the VMAF proxy.

IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, Korea

http://www.vcip2023.org/

[PDF][Slides][Video]

Yiqun Liu (Ateme), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohsen Abdoli (IRT b-com),  Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Thomas Guionnet (Ateme)

Abstract: The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like VVC poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in VMAF. This improvement is observed when utilizing VVenC, an open and optimized VVC encoder implementation.

IEEE VCIP 2023: International Conference on Visual Communications and Image Processing

4 – 7 December 2023 | Jeju, Korea

Conference Website

[PDF][Slides]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Prajit T Rajendran (Universite Paris-Saclay),
Samira Afzal (Alpen-Adria-Universität Klagenfurt), Klaus Schoeffmann (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission.
To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., HEVC, AV1) that lie below the predicted rate-distortion curve of the AVC codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.

CSI Magazine Award for Best Sustainability Project or Initiative to GAIA team

We are proud to announce that our project GAIA  was highly commended at the CSI Magazine Awards for best sustainability initiative!  This award recognizes some of the work taking place in the video streaming process to reduce the environmental impact of technology and to drive the world’s green transition.

GAIA (Greek goddess of Earth, mother of all life, personification of the Earth) is a cooperative project between Bitmovin and Alpen-Adria-Universität Klagenfurt (AAU) that aims to enable development of more climate-friendly video streaming platforms by providing complete energy awareness and accountability throughout the entire video delivery chain. GAIA uses innovative technologies such as  Video Encoding Matching-based Model for Cloud and Edge Computing Instances, Green Per-Title Encoding,  and Designing Energy Efficient Player to minimizing the energy consumption and carbon footprint while maintaining the Quality of Experience for users.

We would like to thank the CSI Magazine Awards for this recognition and our partner, Bitmovin, and collaborators for their contribution and support to GAIA. We hope that GAIA will inspire more initiatives and projects that aim to make a positive difference for our planet.

You can find more information about GAIA on our website

Community-Based QoE Enhancement for User-Generated Content Live Streaming

13th International Conference on Computer and Knowledge Engineering (ICCKE),

Mashad, Iran, November 1-2, 2023

[PDF][Slides]

Reza Saeedinia (University of Tehran), S. Omid Fatemi (University of Tehran),Daniele Lorenzi (Alpen-Adria Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria Universität Klagenfurt),  Christian Timmerer (Alpen-Adria Universität Klagenfurt)

Live user-generated content (UGC) has increased significantly in video streaming applications. Improving the quality of experience (QoE) for users is a crucial consideration in UGC live streaming, where a user can be both a subscriber and a streamer. Resource allocation is an NP-complete task in UGC live streaming due to many subscribers and streamers with varying requests, bandwidth limitations, and network constraints. In this paper, to decrease the execution time of the resource allocation algorithm, we first process streamers’ and subscribers’ requests and then aggregate them into a limited number of groups based on their preferences. Second, we
perform resource allocation for these groups that we call communities. We formulate the resource allocation problem for communities into an optimization problem. With an efficient aggregation of subscribers and streamers at the core of the proposed architecture, the computational complexity of the optimization problem is reduced, consequently improving QoE. This improvement occurs because of the prompt reaction to the bandwidth fluctuations and, subsequently, appropriate resource allocation by the proposed model. We conduct experiments in various scenarios. The results show an average of 41% improvement in execution time. To evaluate the impact of bandwidth fluctuations on the proposed algorithm, we employ two network traces: AmazonFCC and NYUBUS. The results show 4%, and 28% QoE improvement in a scenario with 5
streamers over the AmazonFCC and the NYUBUS network traces, respectively

ARTEMIS: Adaptive Bitrate Ladder Optimization for Live Video Streaming

[PDF][Slides]

Farzad Tashtarian (Alpen-Adria Universität Klagenfurt),  Abdelhak Bentaleb (Concordia University), Hadi Amirpour (Alpen-Adria Universität Klagenfurt) , Sergey Gorinsky (IMDEA Networks Institute),  Junchen Jiang (University of Chicago), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt) , Christian Timmerer (Alpen-Adria Universität Klagenfurt)

Live streaming of segmented videos over the Hypertext Transfer Protocol (HTTP) is increasingly popular and serves heterogeneous clients by offering each segment in multiple representations. A bitrate ladder expresses this choice as an ordered list of bitrate-resolution pairs. Whereas existing solutions for HTTP-based live streaming use a static bitrate ladder, the fixed ladders struggle to appropriately accommodate the dynamics in the video content and network-conditioned client capabilities. This paper proposes ARTEMIS as a practical scalable alternative that dynamically configures the bitrate ladder depending on the content complexity, network conditions, and clients’ statistics. ARTEMIS seamlessly integrates with the end-to-end streaming pipeline and operates transparently to video encoders and clients. We develop a cloud-based implementation of ARTEMIS and conduct extensive real-world and trace-driven experiments. The experimental comparison vs. existing prominent bitrate ladders demonstrates that live streaming with ARTEMIS outperforms all baselines, reduces encoding computation by 25%, end-to-end latency by 18%, and increases quality of experience (QoE) by 11%.