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

Title: EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training

Authors: Yiying Wei, Hadi Amirpour, Jong Hwan Ko, and Christian Timmerer

Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth limits. Existing approaches transmit overfitted super-resolution (SR) model streams for low-resolution (LR) bitstreams, which are used to reconstruct high-resolution (HR) videos at the decoder. Although these approaches show promising results, the huge computational costs of training a large number of video frames limit their practical applications. To overcome this challenge, we propose an efficient patch sampling method named EPS for video SR network overfitting, which identifies the most valuable training patches from video frames.

To this end, we first present two low-complexity Discrete Cosine Transform (DCT)-based spatial-temporal features to measure the complexity score of each patch directly. By analyzing the histogram distribution of these features, we then categorize all possible patches into different clusters and select training patches from the cluster with the highest spatial-temporal information. The number of sampled patches is adaptive based on the video content, addressing the trade-off between training complexity and efficiency.

Our method reduces the number of training patches by 75.00\% to 91.69\%, depending on the resolution and number of clusters, while preserving high video quality and greatly improving training efficiency. Our method speeds up patch sampling by up to 82.1$\times$ compared to the state-of-the-art patch sampling technique (EMT).

Hadi

Paper accepted @ IEEE Transactions on Image Processing (IEEE TIP)

Title: Perception-Inspired Network for Stereo Image Quality Assessment

Authors: Yongli Chang, Guanghui Yue, Bo Zhao, Li Yu, Yakun Ju,  Hadi Amirpour,  Moncef Gabbouj and Wei Zhou.

Abstract: Existing stereo image quality assessment (SIQA) methods generally have limitations in binocular fusion and fine-grained perception modeling. To address these issues, we propose a Perception-Inspired Network for SIQA that simulates binocular difference-guided fusion, high-frequency sensitivity, and hierarchical perception mechanisms of the human visual system (HVS). First, a difference-guided binocular fusion (DGBF) module is designed to mimic the binocular difference sensitivity mechanism, which exploits difference information at both the feature-level and image-level to optimize binocular fusion. Furthermore, the image distortion primarily affects the high-frequency components, which are critical for perceptual quality. To reflect this, we propose a high-frequency enhancement module (HFEM) to simulate the human eye’s sensitivity to edge and texture distortions. Finally, to better achieve fine-grained perception modeling, we propose a hierarchical quality regression strategy that simulates the human perceptual process, from perceiving local details to forming a global quality judgment, thereby achieving a quality prediction more aligned with human subjective evaluation. Experimental results demonstrate that the proposed method outperforms mainstream approaches, achieving a PLCC of 0.9734 on the LIVE I database, and a PLCC of 0.9632 on the LIVE II database.

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Paper accepted @ Text2Story 2026

Title: Dynamic Participatory Game Design with Local AI: From Interviews to Trauma-Aware Interactive Narratives

Authors: Kseniia Harshina, Tom Tucek, Mathias Lux

Location: TextStory 2026 – Delft, The Netherlands, March 2026

Abstract: We present a work-in-progress, trauma-aware participatory storytelling pipeline that uses a locally hosted large language model (LLM) as a neutral chatbot interviewer. The system supports self-paced narration without cloud processing, prioritizing privacy, data sovereignty, and participant control. Interview transcripts are transformed into a structured scene representation (extracted fields and dialogue prompts), which is then replayed through a lightweight prototype interface as an initial step toward interactive memory-based experiences. We report a small formative expert evaluation (n=2) focusing on perceived comfort, emotional safety, and usability. Participants described the interviewer as low-pressure and reflective, while highlighting limitations such as weak acknowledgement of long answers and occasional “forced turns.” We discuss design implications for narrative extraction, turn-taking, and staged evaluation in sensitive contexts, and outline next steps for community-informed studies with participants who have lived experience of displacement.

Paper accepted @ IEEE Access

Title: Lightweight WebAssembly-Based Intrusion Detection for Zero Trust Edge Networks

Authors: Jonathan Weber (TU Wien, Austria), Ilir Murturi (University of Prishtina, Kosova), Xhevahir Bajrami (University of Prishtina, Kosova), Reza Farahani (University of Klagenfurt, Austria), Praveen Kumar Donta (Stockholm University, Sweden), Schahram Dustdar (TU Wien, Austria)

Venue: IEEE Access

Abstract: IoT devices deployed across computing continuum infrastructures present significant security challenges due to resource constraints and decentralization. Traditional centralized intrusion detection systems struggle in such environments because of limited connectivity, high latency, and single points of failure. To address these challenges, this article extends a learning-driven Zero Trust framework tailored to resource-constrained edge environments and proposes an approach for evaluating lightweight intrusion detection models in such environments. Our extended approach enables systematic evaluation of lightweight machine learning models for localized intrusion detection, comprising three layers: (i) compilation, (ii) execution, and (iii) measurement. The proposed approach is implemented using Rust and WebAssembly to ensure portable, efficient, and isolated execution across heterogeneous devices. Using this framework, seven representative intrusion detection models (i.e., Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (KNN), Logistic Regression (LR), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) variants) were implemented and evaluated on the UNSW-NB15 dataset. Results show that RF achieved the best trade-off between detection accuracy and efficiency, while simpler models (DT and LR) offered near-instant inference with minimal resource usage, making them ideal for highly constrained devices. In contrast, more complex models such as deep neural networks and KNN introduced significant overhead for only modest accuracy gains. These findings underscore the need to balance accuracy and resource efficiency for effective Zero Trust edge security.

Paper accepted @ Internet Technology Letter

Title: Performance Evaluation of Privacy Models for Data Streams on the Edge

Authors: Ilir Murturi, Boris Sedlak, Reza Farahani, Schahram Dustdar

Venue: Internet Technology Letter

Abstract: Recent advances in edge computing enable data stream privacy enforcement directly on resource‐constrained devices, reducing latency and the exposure of sensitive information. In this paper, we extend and validate our previously proposed privacy‐enforcing framework, which allows high‐level privacy policies to be expressed as chains of triggers and transformations, executed at the edge. To assess its practical viability, we conduct a comprehensive performance profiling of multiple privacy models across heterogeneous edge hardware platforms. Six privacy‐model chains, ranging from basic face detection to combined face‐and‐person anonymization, are evaluated across three representative edge devices. Key performance metrics (i.e., execution time, CPU utilization, memory usage, and power consumption) are measured to inform optimal placement of privacy transformations. Our evaluation offers critical insights into the effectiveness of the privacy‐enforcing framework on resource‐constrained devices, thereby guiding practitioners in selecting suitable deployment targets for privacy‐preserving data stream analytics on the edge.

Introduction to the Special Issue on ACM Multimedia Systems 2024 and Co-Located Workshops

ACM Transactions on Multimedia Computing, Communications and Applications

[PDF]

Authors: Christian Timmerer (AAU, Austria), Maria Martini (Kingston University London, UK), Ali C. Begen (Ozyegin University, Türkiye), Luca De Cicco (Politecnico di Bari, Italy)

Abstract: This special issue presents recent advances in multimedia systems research showcased at ACM Multimedia Systems 2024 and its co-located workshops. The selected papers span adaptive and immersive video streaming, low-latency and scalable delivery architectures, and innovations in video coding and processing. Together, they illustrate the rapid progress and broad impact of emerging techniques across the multimedia stack.

Paper accepted @ MMSys´26

Paper title: AI-Assisted Energy-Efficient Multimedia Systems

Authors: Zoha Azimi

Venue:  (Doctoral Symposium) MMSys’26, The 17th ACM Multimedia System Conference, Hong Kong SAR, 4th – 8th April 2026

Abstract: Video streaming constitutes the majority of today’s Internet traffic and is expected to continue growing in scale, complexity, and environmental impact. As video systems, from encoding to delivery, consume substantial computational resources, their energy footprint has emerged as a critical challenge for both industry and research communities. At the same time, recent advances in Artificial Intelligence (AI) and Generative AI have improved video quality and adaptive streaming performance, yet often at the cost of increased computational load and higher energy consumption. This increase creates a growing need for streaming systems that not only deliver high Quality of Experience (QoE) but also minimize energy usage across heterogeneous devices and network infrastructures. The scope of this doctoral study is within the end-to-end video ecosystem, focusing on balancing the trade-off between energy consumption and user experience. It aims to investigate and develop intelligent frameworks that treat sustainability as a primary metric alongside performance, improving energy efficiency across the video lifecycle without compromising the viewer’s experience. We present three fundamental research questions to target the related challenges in the domain of sustainable multimedia systems.

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Paper accepted @ The 3rd Geogames Symposium (3GGS) Best, Kseniia

Ttitle: Tell Your Story Through Games (TYS):  Preliminary Guidelines for Mixed-Migrant Participatory Game Jams

Conference: The 3rd Geogames Symposium (3GGS) – Iowa, USA

Authors: Kseniia Harshina

Abstract: Migration-themed games are often framed as “empathy machines,” inviting outsiders to temporarily inhabit another’s hardship. This framing can slide into identity tourism and reinforce the trope of the “helpless refugee”. This short paper presents Tell Your Story Through Games (TYS) as a participatory game jam method that centers agency, and self-expression in mixed-migrant communities. We contribute preliminary methodological guidelines for running participatory story game jams with mixed-migrant communities. Participants with lived experience of migration and/or displacement create place-based story games, choosing how personal or fictional they want the story to be. One pilot iteration has been completed; future iterations will refine the method and evaluate its support for narrative agency and community-building.

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Paper accepted @ DiGRA 2026

Title: Pleasure Not For Everyone: Epistemic Injustice Towards Ukrainian Game Studies

Conference: 17th Annual DiGRA Conference

Authors: Kseniia Harshina, Mark Maletska

Abstract: Calls for playfulness in research often emphasize joy and learning, yet play and games can also exclude and harm minoritized participants—including within academia. In game studies, debates on diversity and postcoloniality have grown, but they still tend to center Western/Western European perspectives, which can obscure other forms of marginalization inside “Europe,” including Eastern Europe. This extended abstract summarizes an in-progress collaborative autoethnography by Ukrainian game studies scholars. Using Fricker’s concept of epistemic injustice, we argue that Ukrainian scholars face a dual barrier to participation in game studies: (1) structural inaccessibility within Ukraine (e.g., disrupted institutions, limited resources and mobility) and (2) epistemic misrecognition within Western institutions (e.g., being treated as subjects of humanitarian concern rather than credible theory producers). Together, these barriers shape who can enter the field and which knowledge is considered legitimate.

 

Paper accepted @ Expert Systems With Applications

Adaptive Compressed Domain Video Encryption

Expert Systems With Applications

Mohammad Ghasempour (AAU, Austria), Yuan Yuan (Southwest Jiaotong University), Hadi Amirpour (AAU, Austria), Hongjie He (Southwest Jiaotong University), and Christian Timmerer (AAU, Austria)

Abstract: With the ever-increasing amount of digital video content, efficient encryption is crucial to protect visual content across diverse platforms. Existing methods often incur excessive bitrate overhead due to content variability. Furthermore, since most videos are already compressed, encryption in the compressed domain is essential to avoid processing overhead and re-compression quality loss. However, achieving both format compliance and compression efficiency while ensuring that the decoded content remains unrecognizable is challenging in the compressed domain, since only limited information is available without full decoding. This paper proposes an adaptive compressed domain video encryption (ACDC) method that dynamically adjusts the encryption strategy according to content characteristics. Two tunable parameters derived from the bitstream information enable adaptation to various application requirements. An adaptive syntax integrity method is employed to produce format-compliant bitstreams without full decoding. Experimental results show that ACDC reduces bitrate overhead by 48.2% and achieves a 31-fold speedup in encryption time compared to the latest state of the art, while producing visually unrecognizable outputs.