Description
Summary
In this internship at the London AI Video Lab, the objective is to study fixed-point arithmetic solutions for ensuring bit-exact video compression in AI-based video codecs. Current AI-based video compression models outperform conventional codecs, like HEVC, VVC and AV1. However, AI-based video compression models are trained using floating-point arithmetic. Unfortunately, floating point arithmetic is insufficient to ensure bit-exact execution. Bit-exact execution is needed to ensure encoded bitstreams are universally decodable across any device. Fixed-point arithmetic is a potential solution to this problem. The goal of the internship is to determine a fixed-point arithmetic setup capable of ensuring bit-exactness while maintaining model performance.
This work will be seen as one step forward toward the deployment of end-to-end trained AI-based video compression models.
The goal will be to study various fixed-point arithmetic setups for layers and components of AI-based video compression models. Quantization bit-width, scaling and bias will be studied on a per component basis. The setup will be integrated into the London AI Video Lab's end-to-end trained video compression model. The performance of the proposed solution will be evaluated and compared to existing models.
The internship will take place in the London AI Video Lab. The intern will be mentored by scientists and will be part of a research project developing end-to-end trained AI-based video compression models.
Duration: 5-6 months, starting January-April 2026
Responsibilities
State-of-the-art and analysis of existing solutions
Implementation of deterministic fixed-point Deep Learning layers with varying bit-depths
Evaluation and reporting of results
Related work
Nagel, Markus, et al. "A white paper on neural network quantization." arXiv preprint arXiv:21 (2021).
Jia, Zhaoyang, et al. "Towards practical real-time neural video compression." Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.
Li, Zhikai, Gu, Qingyi I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2023.
Keywords: scientific computing, computer vision, video compression, machine learning (deep learning), real-time video processing
Expected Outcomes:
Apart from the expected outcome that corresponds to the bit-exact model and its evaluation, this internship will be expected to generate patents and publications.
Date de début
15 déc., 2025
Profil
Qualifications
List minimum required qualifications, preferred skills, abilities, experience, and education
MSc in Computer Science, Machine Learning, Mathematics, Physics or a related field
Fluency in C++ and Python, video processing, computer vision, PyTorch
Répartition du temps de travail
Full time
Durée (Mois)
6
Formation
RJ/Qualif/Ingenieur_B5
Secteur
Ind_hightech_telecom