Aymen Sekhri
About
I am a PhD candidate jointly affiliated with the Norwegian University of Science and Technology (NTNU), Gjøvik, and the University of Poitiers, France, where I work on subjective and objective quality assessment for Augmented Reality (AR).
My research lies at the intersection of artificial intelligence, computer vision, and augmented reality, focusing on developing machine learning models to evaluate and enhance visual experiences in AR and VR environments. During my engineering studies, I also worked on medical imaging, which strengthened my interest in applying AI to real-world perceptual and visual challenges.
At NTNU’s Colourlab, I am involved in designing large-scale AR subjective experiments and developing transformer-based deep learning models for image quality assessment (IQA). My broader interests include representation learning, self-supervised learning, and AI for immersive media.
I have published research in IEEE ICIP, ICASSP, MMSP, and EUSIPCO, and I am expected to complete my PhD in October 2026.
Beyond research, I value collaborative, cross-disciplinary work that bridges AI, human–computer interaction and Medical Imaging.
Competencies
- AgenticAI
- Artificial Intelligent
- Artificial intelligence
- Augmented Reality
- Augmented Reality
- Computer Vision
- Computer science
- Data Analysis
- Data Collection
- Data Processing
- Deep Learning
- Deep learning
- Immersive Media
- LLMs
- Machine Learning
- Machine learning
- Medical Imaging
- Medical Imaging
- Python
- Pytorch
- Representation Learning
- Research
- Self-Supervised Learning
- Software Engineering
- Software engineering
Publications
[1] A. Sekhri, S. A. Amirshahi, and M.-C. Larabi, “Towards light-weight transformer-based quality assessment metric for augmented reality,” in Proc. IEEE 26th Int. Workshop on Multimedia Signal Processing (MMSP), 2024, pp. 1–6.
[2] A. Sekhri, M.-C. Larabi, and S. A. Amirshahi, “Lightweight image quality prediction guided by perceptual ranking feedback,” in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2025, pp. 1–5.
[3] A. Sekhri, M.-C. Larabi, and S. A. Amirshahi, “ARaBIQA: A novel blind image quality assessment model for augmented reality,” in Proc. IEEE Int. Conf. on Image Processing (ICIP), 2025, pp. 379–384.
[4] A. Sekhri, M. A. Kerkouri, A. Chetouani, M. Tliba, Y. Nasser, R. Jennane, and A. Bruno, “Automatic diagnosis of knee osteoarthritis severity using Swin Transformer,” in Proc. 20th Int. Conf. on Content-Based Multimedia Indexing (CBMI), 2023, pp. 41–47.
[5] A. Sekhri, S. A. Amirshahi, and M.-C. Larabi, “Enhancing content representation for AR image quality assessment using knowledge distillation,” arXiv preprint arXiv:2412.06003, 2024.
[6] M. Tliba, A. Sekhri, M. A. Kerkouri, and A. Chetouani, “Deep-based quality assessment of medical images through domain adaptation,” in Proc. IEEE Int. Conf. on Image Processing (ICIP), 2022, pp. 3692–3696.
[7] A. Sekhri, M. Tliba, M. A. Kerkouri, Y. Nasser, A. Chetouani, A. Bruno, and R. Jennane, “Shifting focus: From global semantics to local prominent features in Swin Transformer for knee osteoarthritis severity assessment,” in Proc. 32nd Eur. Signal Process. Conf. (EUSIPCO), 2024, pp. 1686–1690.
[8] A. Bruno, P. Oza, F. Adedoyin, M. Tliba, M. A. Kerkouri, A. Sekhri, A. Chetouani, and M. Gao, “Do digital images tell the truth?” in Digital Image Security, CRC Press, 2024, pp. 247–265.
2025
-
Sekhri, Aymen;
Larabi, Mohamed Chaker;
Amirshahi, Seyed Ali.
(2025)
Lightweight Image Quality Prediction Guided by Perceptual Ranking Feedback.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
Academic article
Journal publications
-
Sekhri, Aymen;
Larabi, Mohamed Chaker;
Amirshahi, Seyed Ali.
(2025)
Lightweight Image Quality Prediction Guided by Perceptual Ranking Feedback.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
Academic article