The 1st IEEE ICME Workshop on Surpassing Latency Limits in Adaptive Live Video Streaming (LIVES’24)

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Patent Approval for “Low-Latency Online Per-Title Encoding”

Low-Latency Online Per-Title Encoding

US Patent


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 online per-title encoding. A method for online per-title encoding includes receiving a video input, generating segments of the video input, extracting a spatial feature and a temporal feature, predicting bitrate-resolution pairs based on the spatial feature and the temporal feature, using a discrete cosine transform (DCT)-based energy function, and per-title encoding segments of the video input for the predicted bitrate-resolution pairs. A system for online per-title encoding may include memory for storing a set of bitrates, a set of resolutions, and a machine learning module configured to predict bitrate resolution pairs based on low-complexity spatial and temporal features.


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EUSIPCO’24 Special Session: Frugality for Video Streaming


32nd European Signal Processing Conference

Special Session: Frugality for Video Streaming

It’s time to take action against the threat of climate change by making significant changes to our global greenhouse gas (GHG) emissions. That includes rethinking how we consume energy for digital technologies, and video in particular. Indeed, video streaming technology alone is responsible for over half of digital technology’s global impact. With the rise of digital and remote work becoming more common, there’s been a rapid increase in video data volume, processing, and streaming. Unfortunately, this also means an increase in energy consumption and GHG emissions.

The goal of this special session is to gather the most recent research works dealing with the objective of reducing the impact of video streaming. It includes contributions to reducing the energy cost of generating, compressing, storing, transmitting, and displaying video data. The special session also aims to include works that target global video volume reduction (even by questioning our video usage). Finally, this special session is also dedicated to works that propose reliable models for estimating the video streaming energy cost.

Submission guidelines can be found here and the actual paper submission is here.

Important dates:

  • Full paper submission Mar. 310, 2024
  • Paper acceptance notification May 22, 2024
  • Camera-ready paper deadline Jun. 1, 2024
  • 3-Minute Thesis contest Jun. 15, 2024


  • Thomas Maugey, Senior Researcher at Inria, Rennes, France
  • Cagri Ozcinar, MSK AI, UK
  • Christian Timmerer, Alpen-Adria-Universität, Klagenfurt, Austria


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ICIP 2024 Grand Challenge on Video Complexity

IEEE International Conference on Image Processing (IEEE ICIP)

Grand Challenge on

Video Complexity

27-30 October 2024, Abu Dhabi, UAE



  • Ioannis Katsavounidis (Meta, USA)
  • Hadi Amirpour (AAU, Austria)
  • Ali Ak (Nantes Univ., France)
  • Anil Kokaram (TCD, Ireland)
  • Christian Timmerer (AAU, Austria)


Abstract: Video compression standards rely heavily on eliminating spatial and temporal redundancy within and across video frames. Intra-frame encoding targets redundancy within blocks of a single video frame, whereas inter-frame coding focuses on removing redundancy between the current frame and its reference frames. The level of spatial and temporal redundancy, or complexity, is a crucial factor in video compression. Generally, videos with higher complexity require a greater bitrate to maintain a specific quality level. Understanding the complexity of a video beforehand can significantly enhance the optimization of video coding and streaming workflows. While Spatial Information (SI) and Temporal Information (TI) are traditionally used to represent video complexity, they often exhibit low correlation with actual video coding performance. In this challenge, the goal is to find innovative methods that can quickly and accurately predict the spatial and temporal complexity of a video, with a high correlation to actual performance. These methods should be efficient enough to be applicable in live video streaming scenarios, ensuring real-time adaptability and optimization.


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Beyond Curves and Thresholds – Introducing Uncertainty Estimation to Satisfied User Ratios for Compressed Video

Picture Coding Symposium (PCS) 

12-14 June 2024, Taichung, Taiwan


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

Abstract: Just Noticeable Difference (JND) establishes the threshold between two images or videos wherein differences in quality remain imperceptible to an individual. This threshold, collectively known as the Satisfied User Ratio (SUR), holds significant importance in image and video compression applications, ensuring that differences in quality are imperceptible to the majority (p%) of users, known as p%SUR. While substantial efforts have been dedicated to predicting the p%SUR for various encoding parameters (e.g., QP) and quality metrics (e.g., VMAF), referred to as proxies, systematic consideration of the prediction uncertainties associated with these proxies has hitherto remained unexplored. In this paper, we analyze the uncertainty of p%SUR through Confidence Interval (CI) estimation and assess the consistency of various Video Quality Metrics (VQMs) as proxies for SUR. The analysis reveals challenges in directly using p%SUR as ground truth for training models and highlights the need for uncertainty estimation for SUR with different proxies.

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ICIP 2024 Grand Challenge on 360° Video Super Resolution

IEEE International Conference on Image Processing (IEEE ICIP)

Grand Challenge on

360° Video Super Resolution and Quality Enhancement

27-30 October 2024, Abi Dhabi, UAE


Abstract: Omnidirectional visual content, commonly referred to as 360-degree images and videos, has garnered significant interest in both academia and industry, establishing itself as the primary media modality for VR/XR applications. 360-degree videos offer numerous features and advantages, allowing users to view scenes in all directions, providing an immersive quality of experience with up to 3 degrees of freedom (3DoF). When integrated on embedded devices with remote control, 360-degree videos offer additional degrees of freedom, enabling movement within the space (6DoF). However, 360-degree videos come with specific requirements, such as high-resolution content with up to 16K video resolution to ensure a high-quality representation of the scene. Moreover, limited bandwidth in wireless communication, especially under mobility conditions, imposes strict constraints on the available throughput to prevent packet loss and maintain low end-to-end latency. Adaptive resolution and efficient compression of 360-degree video content can address these challenges by adapting to the available throughput while maintaining high video quality at the decoder. Nevertheless, the downscaling and coding of the original content before transmission introduces visible distortions and loss of information details that cannot be recovered at the decoder side. In this context, machine learning techniques have demonstrated outstanding performance in alleviating coding artifacts and recovering lost details, particularly for 2D video. Compared to 2D video, 360-degree video presents a lower angular resolution issue, requiring augmentation of both the resolution and the quality of the video. This challenge presents an opportunity for the scientific research and industrial community to propose solutions for quality enhancement and super-resolution for 360-degree videos.


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Prof. Mohammad Ghanbari (1948-2024)

In the wake of the passing of Prof. Mohammad Ghanbari, we extend our deepest condolences to his family during this challenging time. Prof. Ghanbari was a distinguished member of our Christian Doppler Laboratory ATHENA since its inception, and we consider ourselves privileged to have had the opportunity to collaborate with him. His contributions, reflected in over 30 joint publications in video coding and streaming, have been accepted at renowned publication venues such as IEEE TCSVT, ACM TOMM, IEEE TIP, IEEE TNSM, IEEE ICIP, IEEE ICASSP, IEEE ICME, ACM MMSys, PCS, IEEE MMSP, among others. Prof. Ghanbari played a pivotal role in the success of our research endeavors, and his profound knowledge, insightful input, and invaluable guidance were consistently valued.

The entire Institute for Information Technology, especially those at the Christian Doppler Laboratory ATHENA, feels deeply saddened by the loss of Prof. Ghanbari. As we come to terms with this period of mourning, reflection, and farewell, we extend our warmest wishes and heartfelt sympathies to his family and the wider research community.

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