Title: Perceptual Reliability in Multimedia: Quality Assessment and Anomaly Analysis
Event: ACM MM 2026, Rio de Janeiro, Brazil — 10–14 November 2026.
Presenters: Wei Zhou, Hadi Amirpour, Yang Liu, Patrick Le Callet
Title: Asymmetry-Aware No-Reference Video Quality Assessment via Dual-Region Temporal Modeling
Authors: MohammadAli Hamidi, Hadi Amirpour, Christian Timmerer, Luigi Atzori
Abstract: Saliency and semantic-driven asymmetric encoding enable significant bitrate savings while maintaining a comparable viewing experience. This paper presents a No-Reference (NR) Video Quality Assessment (VQA) model for evaluating Asymmetrically Encoded Videos (AEV), addressing challenges such as varying compression levels, scaling artifacts, and asymmetric encoding strategies. The proposed approach combines compression-aware features derived from Quantization Parameters (QPs) with spatio-temporal perceptual descriptors capturing blur, motion, and temporal consistency. A hybrid regression framework based on XGBoost and Ridge regression is employed, where a weighted ensemble improves overall performance. Experimental results conducted on the dataset provided by the QoMEX VQA-AEV Grand Challenge, evaluated under a Leave-One-Source-Out (LOSO) protocol, show that the proposed method outperforms state-of-the-art NR-VQA models in terms of correlation coefficients (Pearson and Spearman) and root mean square error (RMSE).
Title: Asymmetry-Aware No-Reference Video Quality Assessment via Dual-Region Temporal Modeling
Authors: Yeganeh Chatri, Hadi Amirpour
Abstract: Modern content-adaptive video encoding increasingly relies on asymmetric compression, where semantically important regions are preserved at higher quality than background areas. This results in spatially and temporally heterogeneous distortion patterns that challenge conventional no-reference video quality assessment (NR-VQA) models, which typically assume spatial homogeneity.
In this work, we propose a lightweight dual-region NR-VQA framework that explicitly models distortion heterogeneity by jointly analyzing global context and a content-focused region using a shared ResNet-18 backbone with temporal mean aggregation. To address limited training data, a two-stage freeze–unfreeze optimization strategy is employed for stable learning.
Experiments on the QoMEX Grand Challenge dataset show that the proposed method achieves an SROCC of 0.881, the highest among the evaluated NR-VQA baselines in our experiments, including NIQE, BRISQUE, DOVER, and Q-Align. Additional evaluations on KoNViD-1k and LIVE-VQC indicate consistent generalization across datasets. These results highlight that explicit modeling of spatial heterogeneity is an effective and practical design principle for NR-VQA under asymmetric compression scenarios.
Title: Quality of Multimedia Experience Meets Machine Intelligence
Authors: Wei Zhou, Hadi Amirpour, Tobias Hossfeld
Abstract: Multimedia systems are evolving towards AI-driven, adaptive services, leading to a natural convergence of QoE and machine intelligence. In this context, machine intelligence can empower QoE through learning-based, context-aware, and semantic-driven modelling and optimization. At the same time, QoE can guide machine intelligence by providing a human-centred objective for AI system design and evaluation; see also [11]. Looking beyond human perception, toward agent-centric and hybrid QoE, future multimedia systems increasingly require unified experience objectives that support human-AI co-experience. QoMEX’26 in Cardiff stands as a major milestone highlighting the convergence of Quality of Multimedia Experience with Machine Intelligence. This column reflects on this evolution and outlines the key challenges ahead.
Title: DAP-Adapter: Enhancing Few-Shot CLIP with Dynamically Diverse and Context-Aware Prompt Generation
Authors: Zongjian Li, Hongyou Chen, Lingfeng Qu, Yongjie Zhu, Ya Pan, Baodan Tian, Yong Fan, Hadi Amirpour
Abstract: Contrastive language-image pretraining (CLIP) has demonstrated powerful zero-shot and few-shot classification capabilities by training on large-scale image-text pairs. However, in the CLIP training paradigm, data augmentation strategies are applied primarily to the image inputs, whereas the text prompts remain fixed throughout the training process. Existing approaches typically rely on static text templates or use a limited number of learnable soft prompts with categories, which restricts the expressiveness of the model in capturing category semantics. In this paper, we propose a novel approach called the dynamic attribute prompt adapter (DAP-Adapter), which leverages large language models to generate diverse textual descriptions. Our approach introduces attributes as intermediate bridges that link categories to their specific descriptions. During training, a batch-level dynamic language mode sampling mechanism is adopted in combination with learnable soft prompts to dynamically construct rich text prompts. To further enhance its ability to capture semantics, DAP-Adapter also integrates a nontrainable CLIP adapter. To evaluate the model performance, experiments were conducted on ten datasets. The experimental results demonstrate that the proposed DAP-Adapter outperforms the state-of-the-art Tip-Adapter-F method.
Title: QoMEX 2026 Grand Challenge on Video Quality Assessment for Asymmetric Encoded Videos: Methods and Results
Authors: Jingwen Zhu, Hadi Amirpour, Christian Timmerer, et al.
Abstract: This paper presents the results of the Grand Challenge on Video Quality Assessment for Asymmetric Encoded Videos, held at QoMEX 2026 in Cardiff, UK. The challenge addresses the growing need for video quality metrics (VQM) capable of accurately predicting the perceptual quality of asymmetrically encoded videos, where saliency-driven or semantic-based encoding allocates different quality levels to different spatial regions. Participants were provided with the Sport-ROI dataset containing subjective quality scores and were invited to develop both full-reference (FR) and no-reference (NR) VQM models. We describe the challenge design, the dataset, the evaluation methodology, and summarize the submitted approaches and their performance.
EMS 2023 | EMS 2024 | EMS 2025
Call For Submissions
Multimedia has played a significant role in driving Internet usage and has led to a range of technological advancements, such as content delivery networks, compression algorithms, and streaming protocols. With emerging applications, including (but not limited to) augmented, virtual, and extended reality (XR), real-time telepresence, AI-generated content, video analytics, and the usage of AI in multimedia systems in general, multimedia is undergoing a fundamental shift in sharing experiences online and continues to drive the future of the Internet. As these next-generation ultra-low-latency, interactive, and immersive technologies evolve, it is crucial to revisit developed techniques for new formats and representations, not only to enhance performance and interactivity but also to improve energy efficiency and maintain high Quality of Experience (QoE). This workshop will bring together experts from diverse fields, including video streaming research, source video coding, analytics, rate adaptation algorithms, networked systems, immersive media such as 3D and volumetric video streaming, AR/VR applications, as well as energy-efficient systems and QoE optimization, to exchange ideas on identifying challenges and opportunities in designing advanced networked systems for these emerging multimedia technologies. This workshop is a successor of the Emerging Multimedia System (EMS) workshop from ACM SIGCOMM.
Topics of interest in Call-for-Papers
This workshop calls for research on various issues and solutions that can enable live video analytics with the role of edge computing. Topics of interest include (but not limited to) the following:
- Networked systems for immersive content capture, streaming, and display
- Networked systems for AI-driven video applications
- Networked systems for multimedia generative AI
- Machine learning for emerging multimedia distribution
- Emerging multimedia systems for novel content formats (point clouds, light fields, holography, NeRF, 3DGS, etc.)
- Volumetric video delivery on the Internet
- Ultra-low-latency networking for multimedia applications
- High-throughput transport and distribution for emerging media
- Adaptive streaming under network/user constraints for immersive media
- Novel content distribution network for AR/VR applications
- Management of AR/VR networked systems
- Wireless and mobile immersive systems
- AR/VR applications in current (5G) and future (6G) wireless networks
- Compression and transmission design for 3D content
- Edge cloud systems for immersive experiences
- Quality of Experience in emerging multimedia systems
- Energy efficiency in emerging multimedia systems
Besides typical full research papers that present a complete idea with proper evaluation, we also welcome work-in-progress papers.
- Full research papers present new research that has not been previously published. Authors should submit their work describing early/emerging results in a relevant topic area. Full papers are limited to 6 pages, including figures, tables, appendices, and references.
- Lightning papers can provide a summary of early, emerging, or ongoing work, as well as short updates of previously published work. Lightning papers will be presented and published in the proceedings. They should not exceed 2 pages, with a maximum of one additional page for references only.
Important Dates
- Paper Submissions: July 14, 2026
- Acceptance Notification: August 26, 2026
- Camera-ready: September 21, 2026
Organizing Committee
Co-Chairs
- Amr Rizk (Leibniz University Hannover, Germany)
- Farzad Tashtarian (Klagenfurt University, Austria)
- Mallesham Dasari (Northeastern University, USA)
- Maria Martini (Kingston University, UK)
Technical Program Committee
- TBD
Keynote Speech
- TBD
On 27 May 2026, Dr Felix Schniz held a guest presentation on the Transhuman Qualities of Bloodborne at the University of Ljubljana before joining the conference Creative Computing Cultures and Media Transfers in Europe as an invited guest. Following the spirit of cross-European perspectives on computing, the day concluded with a joint meeting on prospective project proposals.
Following an invitation of the Viennese Game Lab, Felix Schniz and Sabrina Maria Größing have represented the Klagenfurt Critical Game Lab at the event “Spielend lernen!” that took place on 19 May 2026 at the Bildungsdirektion Wien. Klagenfurt was the first non-Viennese game lab to join the event underlining the importance of the University of Klagenfurt for Austrian Game Studies outside of Vienna, and able to represent its unique approaches to the challenges of introducing technological literacy and the importance of play to an intrigued audience of experts and pedagogues.
Building on a prior visit of Viennese Game Lab scholars to Klagenfurt, the event was concluded with a tour through the local game lab facilities on 20 May and an extended chat about shared challenges and future opportunities for cooperation.
Cross-Layer Dynamics in Live Low-Latency: A Dataset of ABR, CC, and AQM Interactions
18th International Conference on Quality of Multimedia Experience
Cardiff, UK, June 29th – July 3rd, 2026
[PDF]
Md Tariqul Islam (UNICAMP, Brazil), Farzad Tashtarian (AAU, Austria), Christian Esteve Rothenberg (UNICAMP, Brazil), Christian Timmerer (AAU, Austria).
Low-latency video streaming, such as Low-Latency DASH (LL-DASH), requires maintaining high Quality of Experience (QoE) under varying network conditions. In LL-DASH, QoE is jointly influenced not only by Adaptive Bitrate (ABR) decisions, but also by transport-layer Congestion Control (CC) and network-layer Active Queue Management (AQM), whose interactions remain insufficiently characterized due to limited cross-layer experimentation. Therefore, we present a large-scale LL-DASH dataset comprising approximately 2,000 controlled sessions across three dash.js ABR algorithms (L2A, Dynamic, LoLP), three CC schemes (CUBIC, BBRv1, Prague) across both TCP and QUIC transport protocols, four AQM configurations (FIFO, FQ-CoDel, CAKE, DualPI2), and multiple congestion scenarios. The dataset supports QoE-aware cross-layer analysis and ABR benchmarking under diverse network configurations and is available at: https://github.com/cd-athena/ ll-dash-crosslayer-dataset