Intern, Spatially Sparse AI-based video decoders

About InterDigital

InterDigital is a global research and development company focused primarily on wireless, video, artificial intelligence (“AI”), and related technologies. We design and develop foundational technologies that enable connected, immersive experiences in a broad range of communications and entertainment products and services. We license our innovations worldwide to companies providing such products and services, including makers of wireless communications devices, consumer electronics, IoT devices, cars and other motor vehicles, and providers of cloud-based services such as video streaming. As a leader in wireless technology, our engineers have designed and developed a wide range of innovations that are used in wireless products and networks, from the earliest digital cellular systems to 5G and today’s most advanced Wi-Fi technologies. We are also a leader in video processing and video encoding/decoding technology, with a significant AI research effort that intersects with both wireless and video technologies. Founded in 1972, InterDigital is listed on Nasdaq.

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For more information, visit: www.interdigital.com.

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 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 a computationally efficient AI-based video decoder

• Evaluation and reporting of results

Related work

Graham, Benjamin, and Laurens Van der Maaten. "Submanifold sparse convolutional networks." arXiv preprint arXiv:1706.01307 (2017).

Jia, Zhaoyang, et al. "Towards practical real-time neural video compression." Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.

Parger, Mathias, et al. "Deltacnn: End-to-end cnn inference of sparse frame differences in videos." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

Qualifications

• MSc in Computer Science, Machine Learning, Mathematics, Physics or a related field

• Deep learning, computer vision, Python, PyTorch

Keywords: computer vision, video compression, machine learning (deep learning), real-time video processing

Expected Outcomes:

Apart from the expected outcome that corresponds to the spatially sparse video model and its evaluation, this internship will be expected to generate patents and publications.

Location: London, UK

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