Adaptive Compressed Domain Video Encryption

Expert Systems With Applications

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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.

YTLive: A Dataset of Real-World YouTube Live Streaming Sessions

IEEE/IFIP Network Operations and Management Symposium (NOMS) 2026

 Rome, Italy- 18 – 22 May 2026

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Resource Management for Distributed Binary Neural Networks in Programmable Data Plane

IEEE/IFIP Network Operations and Management Symposium (NOMS) 2026

 Rome, Italy- 18 – 22 May 2026

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QoE Modeling in Volumetric Video Streaming: A Short Survey

IEEE/IFIP Network Operations and Management Symposium (NOMS) 2026

 Rome, Italy- 18 – 22 May 2026

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Volumetric video streaming enables six degrees of freedom (6DoF) interaction, allowing users to navigate freely within immersive 3D environments. Despite notable advancements, volumetric video remains an emerging field, presenting ongoing challenges and vast opportunities in content capture, compression, transmission, decompression, rendering, and display. As user expectations grow, delivering high Quality of Experience (QoE) in these systems becomes increasingly critical due to the complexity of volumetric content and the demands of interactive streaming. This paper reviews recent progress in QoE for volumetric streaming, beginning with an overview of QoE evaluation of volumetric video streaming studies, including subjective assessments tailored to 6DoF content. The core focus of this work is on objective QoE modeling, where we analyze existing models based on their input factors and methodological strategies. Finally, we discuss the key challenges and promising research directions for building perceptually accurate and adaptable QoE models that can support the future of immersive volumetric media.

Perceptual Quality Optimization of Image Super-Resolution

2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2026)

4 – 8 May, 2026

Barcelona, Spain

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Wei Zhou, Yixiao Li, Hadi Amirpour, Xiaoshuai Hao, Jiang Liu, Peng Wang, Hantao Liu

Abstract: Single image super-resolution (SR) has achieved remarkable progress with deep learning, yet most approaches rely on distortion-oriented losses or heuristic perceptual priors, which often lead to a trade-off between fidelity and visual quality. To address this issue, we propose an \textbf{Efficient Perceptual Bi-directional Attention Network (Efficient-PBAN)} that explicitly optimizes SR towards human-preferred quality. The proposed framework is trained on a newly constructed SR quality dataset that covers a wide range of state-of-the-art SR methods with corresponding human opinion scores. Using this dataset, Efficient-PBAN learns to predict perceptual quality in a way that correlates strongly with subjective judgments. The learned metric is further integrated into SR training as a differentiable perceptual loss, enabling closed-loop alignment between reconstruction and perceptual assessment. Extensive experiments demonstrate that our approach delivers superior perceptual quality.

ProgressIQA: Progressive Curriculum and Ensemble Self-Training for Filter-Altered Image Quality Assessment

2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2026)

4 – 8 May, 2026

Barcelona, Spain

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MohammadAli Hamidi, Hadi Amirpour, Christian Timmerer, Luigi Atzori

Abstract: Filter-altered images are increasingly prevalent in online visual communication, particularly on social media platforms. Assessing the relevant perceived quality is essential for effectively managing visual communication. However, the perceived quality is content-dependent and non-monotonic, posing challenges for distortion-centric Image Quality Assessment (IQA) models. The Image Manipulation Quality Assessment (IMQA) benchmark addressed this gap with a dual-stream baseline that fuses filter-aware and quality-aware encoders via an MS-CAM attention module. However, only eight of the ten dataset folds are publicly released, making the task more data-constrained than the original 10-fold protocol. To overcome this limitation, we propose ProgressIQA, a data-efficient framework that integrates ensemble self-training, label distribution stratification, and multi-stage progressive curriculum learning. Fold-specific models are ensembled to generate stable teacher predictions, which are used as pseudo-labels for external filter-augmented images. These pseudo-labels are then balanced through stratified sampling and combined with the original data in a progressive curriculum that transfers knowledge from coarse to fine resolution across stages. Under the restricted 8-fold protocol, ProgressIQA achieves PLCC 0.7082 / SROCC 0.7107, outperforming the IMQA baseline (0.5616 / 0.5486) and even surpassing the original 10-fold evaluation in SROCC (0.7253 / 0.6870).

BiNR: Live Video Broadcasting Quality Assessment

2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2026)

4 – 8 May, 2026

Barcelona, Spain

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Hadi Amirpour, MohammadAli Hamidi, Wei Zhou, Luigi Atzori, Christian Timmerer

Abstract: Live video broadcasting has become widely accessible through popular platforms such as Instagram, Facebook, and YouTube, enabling real-time content sharing and user interaction. While the Quality of Experience (QoE) has been extensively studied for Video-on-Demand (VoD) services, the QoE of live broadcast videos remains relatively underexplored. In this paper, we address this gap by proposing a novel machine learning–based model for QoE prediction in live video broadcasting scenarios. Our approach, BiNR, introduces two models: BiNR_fast, which uses only bitstream features for ultra-fast QoE predictions, and the full model BiNR_full, which integrates bitstream features with a pixel-based no-reference (NR) quality metric that works on the decoded signal.
We evaluate multiple regression models to predict subjective QoE scores and further conduct feature importance analysis. Experimental results show that our full model achieves a Pearson Correlation Coefficient (PCC)/Spearman Rank Correlation Coefficient (SRCC) of 0.92/0.92 with subjective scores, significantly outperforming the state-of-the-art methods.