The Visual Signal Processing and Communications (VSPC) Rising Star Program was established in 2023 by the CAS Society VSPC Technical Committee (TC) to annually recognise an expert(s) in emerging domains of visual signal processing and communications for his/her accomplishments and vision.
This year, Hadi was nominated and will give a 30-minute presentation, followed by a Q&A session, at the upcoming VCIP 2026 Conference, to be held in Singapore from December 13-16, 2026.
The Book, the Board, and the Lantern in the Dark
Violence, Vulnerability, and Intermedial Player Immersion in Kingdom Death: Monster
Where: Cologne University
Conference: Annual conference of the Gesellschaft für Fantastikforschung, September 17-19
Immersion is often understood as an enthralling concept, reserved for complex virtual worlds that veil reality behind an integral mediated experience. I challenge this assumption and argue that analogue games can cultivate immersive experiences, even when they are played with clearly separable material components. Using the narrative boutique board game Kingdom Death: Monster (Poots 2015) as a case study, I demonstrate how the game fosters immersion across different analogue media components by the perpetual evocation of violence against a frail player community.
Relying on the theoretical observations of Paul Wake, who highlights the importance of a board game’s play-performance that fills all indeterminacy with player-created meaning (cf. 2023, 49) and by focusing on the game’s intermedial connotations (via Wolf 1999, Rajewski 2010, and Ellenström 2021), I portray how Kingdom Death: Monster creates immersion between different media artefacts in a reliance on violence. The game at first fosters intimacy through a shared reading moment that leads to a co-op bonding survival gameplay segment, then it effectively exploits both sensations by forcing players into violent discomfort to evoke vulnerability. Both rulebook and play-sequence interact with one another under this shared leitmotif, upheld across two different media artefacts and the metaphoric-aesthetic driving factors of Eros and Thanatos in the sense of a discomfort between deadly threats and uncomfortable intimacy (following Freud [1920] 1955). Ultimately, I thereby demonstrate how the game evokes immersive qualities typically not connected with the abstract, engaging-rather-than-affective, physical medium.
A concluding discussion of the presented findings questions the adequacy of common observations on immersive traits in specifically analogue game studies. By discussing opportunities for methodological finetuning, my presentation’s outlook, in turn, resonates with the idea of a ‘painful playthrough,’ as worked with by Blackburn and Timmell (cf. 2024, 113) and shows how self-experienced stress in a discomforting game moment must be endured for the sake of analysing violent play.
Replaying Japan 2026 also released “conference proceedings”, which include all extended abstracts of talks presented in the conference: https://drive.google.com/file/d/1Rwx0uaOOsluPYUPUlin4Wa-ugW8yRnDW/view
Four Talks Presented at This Year’s VGC in Klagenfurt, 3–5 September 2026
Dr. Kurt Horvath has been elevated to IEEE Senior Member. An IEEE Senior Member is the highest professional grade for which an IEEE member can apply. This distinction requires extensive professional experience and demonstrated accomplishments that reflect technical expertise, leadership, and professional maturity. Fewer than 10% of IEEE’s nearly half a million members worldwide have achieved this honor.
IEEE International Conference on Visual Communications and Image Processing (VCIP 2026), December 13 – December 16, 2026, Singapore
Authors: Kamran Qureshi (AAU, Austria), Hadi Amirpour (AAU, Austria), Christian Timmerer (AAU, Austria)
Abstract: Advances in video coding continue to improve compression efficiency for adaptive bitrate (ABR) streaming. However, improved coding efficiency also changes the rate-distortion characteristics of encoded video, thereby altering the resolution transition points and the resulting bitrate ladder for UHD adaptive streaming. A convex-hull-based evaluation was performed to compare codec-dependent bitrate ladders across H.264/AVC, H.265/HEVC, H.266/VVC, AV1, and AV2 using six UHD sequences from the JVET Common Test Conditions. Experimental results showed that higher codec efficiency systematically shifts resolution transition points toward lower bitrates. Relative to AV1, AV2 reduced the median 2160p-1080p transition bitrate by approximately 3x, while the median 1080p-720p and 720p-540p transition bitrates were approximately 9x lower, allowing higher spatial resolutions to remain optimal over a wider bitrate range. Complexity analysis of the evaluated AVC, HEVC, VVC, and AV1 encoders further showed that improved compression efficiency is accompanied by increased encoding time and energy consumption. These findings demonstrate that advances in video compression influence adaptive streaming by modifying the optimal bitrate ladder rather than simply reducing bitrate requirements.
On September 11, Dr Felix Schniz held a game design workshop at Kwadrat youth centre in Klagenfurt. Modelled after the successful Game Jams initiated by Mathias Lux, the workshop introduced young adults to teamwork, game design basics, and interested them in modes of digital game design. A workshop on video games is already in the planning.
IEEE Transactions on Broadcasting
Authors: Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria), Wei Zhou (Cardiff University, UK), Wassim Hamidouche (TII, UAE) and Christian Timmerer (AAU, Austria)
Abstract: Fluctuations in video segment download rates and resolution switching in HTTP Adaptive Streaming (HAS) make it challenging to maintain a consistent Quality of Experience (QoE). However, the impact of such switching is often underestimated, and broadly applicable mitigation strategies remain underexplored. In the past, content-aware approaches have been introduced, using deep neural networks (DNNs) trained on an individual video segment to enhance its quality. These DNNs, transferred as a model stream alongside the video bitstream, allow clients to improve playback quality. However, transferring model streams adds bitrate overhead and additional architectural components, limiting practical use. Furthermore, supporting a wide range of device capabilities with a single DNN is impractical, as it would require device-specific models for each configuration, an approach that becomes unmanageable with increasing device heterogeneity. In this paper, we propose a new self-training method that enables clients to train content-aware video super-resolution (SR) models locally by leveraging previously downloaded high-quality segments. These segments are downscaled and used to train lightweight DNNs, which are then applied to enhance subsequent lower-quality segments. To keep training efficient and real-time, we select only a few predefined frames and extract the most informative patches using a lightweight sampling strategy. Experiments demonstrate that this approach significantly improves visual quality, with average PSNR gains of 1.07 dB (2× upscaling), 0.43 dB (3×), and 0.58 dB (4×) using ESPCN, a lightweight SR approach. To further validate the effectiveness of our approach, we conducted a series of ablation studies to analyze the contributions of individual components. Real-device measurements and end-to-end HAS simulations further show that self-training requires only 2.9–13.0% of a 4-second segment interval on CPU and 1.1–5.3% on GPU across tested mobile devices, while improving VMAF/QoE with only marginal additional rebuffering compared with generic SR.
IEEE Visual Communications and Image Processing Conference (VCIP 2026) – December 13–16, 2026, Singapore
Authors: Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)
Abstract: Traditional video streaming relies on Adaptive Bitrate (ABR) algorithms that encode videos at fixed bitrate-resolution pairs. As a result, a rate controller is essential to ensure that each encoded representation meets its target bitrate. However, perceptually-aware bitrate ladder construction methods aim to encode videos at a fixed visual quality instead of a fixed bitrate, to avoid under- or over-allocating bits for complex and simple content. In this paper, we propose an efficient quality controller that predicts the Quantization Parameter (QP) required to achieve a target VMAF score for each video segment. The framework supports both CPU-only operation for low-complexity environments and GPU-accelerated inference for improved prediction accuracy. By leveraging content features and target quality levels, our model estimates appropriate QP values without requiring pre-encoding or tight integration with the encoder. For target VMAF scores of 94, 88, and 82, the CPU-only model achieves mean absolute errors (MAEs) of 1.05, 1.24, and 1.34, respectively, comparable to the state-of-the-art errors of 1.14, 1.27, and 1.31, while requiring only a fraction of the computational cost. The GPU-based model further reduces the MAEs to 0.50, 0.49, and 0.47, less than half of the state-of-the-art errors.
