Intern, Deep Learning for Perceptual and Color-Aware Energy-Efficient Images

Location: Rennes, France

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.

InterDigital is a registered trademark of InterDigital, Inc.

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

Summary

The objective of this internship is to design a deep learning model for generating energy-aware images. Methods for producing energy-aware images — that is, images that consume less energy than their original versions when displayed on a screen — already exist in the literature. These approaches typically aim to achieve a trade-off between reducing energy consumption and preserving the visual quality of the resulting images.

However, several research directions remain to be explored

Existing approaches mainly focus on reducing the luminance component of images, while we would also like to investigate the colour dimension.

We would like to explore perceptual loss functions, i.e., losses designed to better reflect the characteristics of the human visual perception system.

Current methods struggle to maintain a satisfactory quality-energy trade-off at high power reduction ratios. We would therefore like to investigate how the research directions above could improve performance in this more challenging range.

The objective is to develop a model that combines these different aspects to improve both visual quality and energy efficiency compared with state-of-the-art approaches.

This work represents a further step towards a more sustainable video streaming chain. In the context of climate change, reducing the environmental footprint of video streaming is an important part of the broader effort to reduce carbon emissions.

The internship will first involve reviewing existing approaches for perceptual quality assessment and colour modelling of images. In a second stage, a deep learning-based model will be designed and implemented to generate energy-aware images using these new features. The performance of the proposed model will then be evaluated in terms of both image quality and energy consumption.

The internship will take place within the Media Services research group at the InterDigital Video Lab. The intern will be mentored by research scientists and will contribute to a research project focused on developing solutions to reduce energy consumption across the video streaming ecosystem.

Duration: 5-6 months, starting January-April 2026

Responsibilities

State-of-the-art and analysis of existing solutions

Implementation of a perceptually-based deep learning model for energy reduction

Evaluation and reporting of results

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Related work

[1] O. Le Meur, C. -H. Demarty and L. Blonde, "Deep-Learning-Based Energy Aware Images," 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023, pp. 590-594, doi: 10.1109/ICIP49359.2023.10222188.

[2] Ameur, Z., Demarty, CH., Ménard, D., Meur, O.L. (2025). 3R-INN: How to Be Climate Friendly While Consuming/Delivering Videos?. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15133. Springer, Cham. https://doi.org/10.1007/978-3-031-73226-3_9

[3] Emmanuel Sampaio, Claire-Hélène Demarty, and Olivier Le Meur. 2024. How to make images less power-hungry: An objective benchmark study. In Proceedings of the Second International ACM Green Multimedia Systems Workshop (GMSys '24). Association for Computing Machinery, New York, NY, USA, 1–7. https://doi.org/10.1145/3652104.3652525

Qualifications

Master 2, 6 months internship

Deep learning. Image and video processing. Python. PyTorch.

Keywords energy-aware images, energy consumption, energy reduction, vision quality metrics, perceptual quality, color imaging, machine learning (deep learning).

Expected Outcomes

Apart from the expected outcome that corresponds to the perceptually enhanced energy-aware model and its evaluation, this internship will be expected to generate patents and publications.

Location

Rennes, France

InterDigital is an equal employment opportunity employer. InterDigital will not engage in or tolerate unlawful discrimination with regard to any employment decision, policy or practice based on a person’s sex, gender, pregnancy (including childbirth, breastfeeding and related medical conditions), age, race, color, religion, creed, national origin, ancestry, citizenship, military status, veteran status, mental or physical disability, medical condition, genetic information, sexual orientation, gender identity or expression, or any other factor protected by applicable federal, state or local  law. This policy applies to all terms and conditions of employment, including, but not limited to, recruiting, hiring, compensation, benefits, training, assignments, evaluations, coaching, promotion, discipline, discharge and layoff.

À propos d'InterDigital

InterDigital est une société internationale de recherche et