The 2nd ACM MM Workshop on Multimedia Computing for Health and Medicine

Website

In health and medicine, an immense amount of data is being generated by distributed sensors and cameras, as well as multimodal digital health platforms that support multimedia, such as audio, video, image, 3D geometry, and text. The availability of such multimedia data from medical devices and digital record systems has greatly increased the potential for automated diagnosis. The past several years have witnessed an explosion of interest, and a dizzyingly fast development, in computer-aided medical investigations using MRI, CT, X-rays, images, point clouds, etc. This proposed workshop focuses on various multimedia computing techniques (including mobile solutions and hardware solutions) for health and medicine, which targets real-world data/problems in healthcare, involves a large number of stakeholders, and is closely connected with people’s health.

Convex Hull Prediction Methods for Bitrate Ladder Construction: Design, Evaluation, and Comparison

ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM)

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Ahmed Telili (INSA, Rennes, France),  Wassim Hamidouce (INSA, Rennes, France), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Sid Ahmed Fezza (INPTIC, Algeira), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Luce Morin (INSA, Rennes, France)

Abstract:

HTTP adaptive streaming (HAS ) has emerged as a prevalent approach for over-the-top (OTT ) video streaming services due to its ability to deliver a seamless user experience. A fundamental component of HAS is the bitrate ladder, which comprises a set of encoding parameters (e.g., bitrate-resolution pairs) used to encode the source video into multiple representations. This adaptive bitrate ladder enables the client’s video player to dynamically adjust the quality of the video stream in real-time based on fluctuations in network conditions, ensuring uninterrupted playback by selecting the most suitable representation for the available bandwidth. The most straightforward approach involves using a fixed bitrate ladder for all videos, consisting of pre-determined bitrate-resolution pairs known as one-size-fits-all. Conversely, the most reliable technique relies on intensively encoding all resolutions over a wide range of bitrates to build the convex hull, thereby optimizing the bitrate ladder by selecting the representations from the convex hull for each specific video. Several techniques have been proposed to predict content-based ladders without performing a costly, exhaustive search encoding. This paper provides a comprehensive review of various convex hull prediction methods, including both conventional and learning-based approaches. Furthermore, we conduct a benchmark study of several handcrafted- and deep learning ( DL )-based approaches for predicting content-optimized convex hulls across multiple codec settings. The considered methods are evaluated on our proposed large-scale dataset, which includes 300 UHD video shots encoded with software and hardware encoders using three state-of-the-art video standards, including AVC /H.264, HEVC /H.265, and VVC /H.266, at various bitrate points. Our analysis provides valuable insights and establishes baseline performance for future research in this field.
Dataset URL: https://nasext-vaader.insa-rennes.fr/ietr-vaader/datasets/br_ladder

The 3rd ACM SIGCOMM Workshop on Emerging Multimedia Systems (EMS) 2025

Counterfeiting Attacks on a RDH-EI scheme based on block-permutation and Co-XOR

ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM)

[PDF]

Fan Chen (Southwest Jiaotong University, China),  Lingfeng Qu (Guangzhou University, China), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Hongjie He (Southwest Jiaotong University, China)

Abstract:

Reversible data hiding in encrypted images (RDH-EI) has gained widespread attention due to its potential applications in secure cloud storage. However, the security challenges of RDH-EI in cloud storage scenarios remain largely unexplored.} In this paper, we present a counterfeiting attack on RDH-EI schemes that utilize block-permutation and Co-XOR (BPCX) encryption. We demonstrate that ciphertext images generated by BPCX-based RDH-EI are easily tampered with to produce a counterfeit decrypted image with different contents imperceptible to the human eye. This vulnerability is mainly because the block permutation key information of BPCX is susceptible to known-plaintext attacks (KPAs). Taking ciphertext images in telemedicine scenarios as an example, we describe two potential counterfeiting attacks, namely fixed-area and optimal-area attacks. We show that the quality of forged decrypted images depends on the accuracy of the estimated block-permutation key under KPA conditions. To improve the invisibility of counterfeit decrypted images, we analyze the limitations of existing KPA methods against BPCX encryption for 2×2 block sizes and propose a novel diagonal inversion rule specifically designed for image blocks. This rule further enhances the accuracy of the estimated block-permutation key. The experiments show that, compared to existing KPA methods, the accuracy of the estimated block-permutation key in the UCID dataset increases by an average of 11.5%. In the counterfeiting attack experiments on Camera’s encrypted image, we successfully tampered with over 80% of the pixels in the target area under the fixed-region attack. Additionally, we achieved a tampering success rate exceeding 90% in the optimal-region attack.

IEEE ICME 2025
June 30, 2025- July 04, 2025

Nantes, France

https://www.2025.ieeeicme.org/tutorials/

Tutorial speakers:

• Hadi Amirpour (University of Klagenfurt)
• Christian Timmerer (University of Klagenfurt)

Tutorial description:

This tutorial provides a comprehensive exploration of the HTTP Adaptive Streaming (HAS) pipeline, covering advancements from content provisioning to content consumption. We begin by tracing the history of video streaming and the evolution of video coding technologies. Attendees will gain insights into the timeline of significant developments, from early proprietary solutions to modern adaptive streaming standards like HAS. A comparative analysis of video codecs is presented, highlighting milestones such as H.264, HEVC, and the latest standard, Versatile Video Coding (VVC), emphasizing their efficiency, adoption, and impact on streaming technologies. Additionally, new trends in video coding, including AI-based coding solutions, will be covered, showcasing their potential to transform video compression and streaming workflows.

Building on this foundation, we explore per-title encoding techniques, which dynamically tailor bitrate ladders to the specific characteristics of video content. These methods account for factors such as spatial resolution, frame rate, device compatibility, and energy efficiency, optimizing both Quality of Experience (QoE) and environmental sustainability. Next, we highlight cutting-edge  advancements in live streaming, including novel approaches to optimizing bitrate ladders without introducing latency. Fast multi-rate encoding methods are also presented, showcasing how they significantly reduce encoding times and computational costs, effectively addressing scalability challenges for streaming providers.

The tutorial further delves into edge computing capabilities for video transcoding, emphasizing how edge-based architectures can streamline the processing and delivery of streaming content. These approaches reduce latency and enable efficient resource utilization, particularly in live and interactive streaming scenarios.

Finally, we discuss the QoE parameters that influence both streaming and coding pipelines, providing a holistic view of how QoE considerations guide decisions in codec selection, bitrate optimization, and delivery strategies. By combining historical context, theoretical foundations, and practical insights, this tutorial equips attendees with the knowledge to navigate and address the evolving challenges in video streaming applications.

Perceptually-aware Online Per-title Encoding for Live Video Streaming

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: Techniques for implementing perceptually aware per-title encoding may include receiving an input video, a set of resolutions, a maximum target bitrate and a minimum target bitrate, extracting content aware features for each segment of the input video, predicting a perceptually aware bitrate-resolution pair for each segment using a model configured to optimize for a quality metric using constants trained for each of the set of resolutions, generating a target encoding set including a set of perceptually aware bitrate-resolution pairs, and encoding the target encoding set. The content aware features may include a spatial energy feature and an average temporal energy. According to these methods only a subset of bitrates and resolutions, less than a full set of bitrates and resolutions, are encoded to provide high quality video content for streaming.

DORBINE Project Approved by FFG.

DORBINE is a project

• Funded by the Austrian Research Promotion Agency FFG
• A cooperative project between AIR6 Systems and Alpen-Adria-Universität
  Klagenfurt (AAU)