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16 déc., 2025
STAGE, Stage
InternIntegerized Ai-Based Video Compression Models H/F
InterDigital
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...
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
Durée (Mois):
6
16 déc., 2025
STAGE, Stage
InternSpatially Sparse Ai-Based Video Decoders H/F
InterDigital
Summary In this internship at the London AI Video Lab, the objective is to design computationally efficient video decoders in an AI-based video compression codec. Current AI-based video compression models outperform conventional codecs, like HEVC, VVC and AV1. However, this comes at the cost of impractical compute requirements: at decode, current AI-based video compression decoders are several orders of magnitude more complex than conventional video compression decoders. The goal of the internship is to design efficient AI-based decoders that leverage spatial sparsity to reduce their computational complexity. 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 find and review potential existing methods of spatial sparsity in AI-based video models. In a second step, spatially sparse AI-based decoders will be designed, implemented and integrated into the London AI Video Lab's end-to-end...
Qualifications MSc in Computer Science, Machine Learning, Mathematics, Physics or a related field Deep learning, computer vision, Python, PyTorch
Durée (Mois):
6
26 oct., 2025
STAGE, Stage
InternApplied Ml Multimedia Internship Position H/F
InterDigital
Summary As an applied ML Multimedia Research Intern, you will work closely with researchers and engineers to address cutting-edge challenges in the field. You will receive dedicated technical mentorship and guidance, ensuring continuous learning and skill development throughout the internship. Each day will provide new opportunities to expand your technical expertise while deepening your understanding of the multimedia domain. Beyond technical growth, this internship will allow you to build valuable connections with domain experts and industry professionals. Depending on the outcomes of your work, you may also have the opportunity to contribute to publications, further advancing your academic and professional profile. Responsibilities List of primary responsibilities Survey the recent advances of state-of-the-art ML-based media compression technologies Conduct literature reviews and stay up to date with state-of-the-art techniques in ML-based media compression, multimedia...
Qualifications List minimum required qualifications, preferred skills, abilities, experience, and education Currently pursuing master or doctoral degree in Machine learning, multimedia, telecommunication Strong background in machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow) Experience in Python/C++ programming and working with research prototypes. Preferred knowledge on multimedia frameworks Preferred experience with multimedia libraries and frameworks such as libav, ffmpeg, GStreamer etc. Familiarity with signal processing, computer vision, or audio/video communication systems. Knowledge of neural compression methods (e.g., learned image/video compression, generative models for media coding) is a plus. Understanding of multimedia coding standards (e.g., H.264/AVC, H.265/HEVC, AV1, VVC) and compression techniques is a plus Problem solving skills Ability to collaborate with others Preferrable demonstrate expertise with proven publication or...
Durée (Mois):
6