Post-Doctoral Research Visit F/M Universality for Deriving Low-Complexity INR for Visual Compression Models

Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

Contexte et atouts du poste

This postdoctoral position is part of the joint research activities between Inria and InterDigital conducted within Nisk.AI, a joint research project focusing on efficient and frugal compression of new media through artificial intelligence and neural network-based coding. Nisk.AI brings together Inria research teams and InterDigital to investigate new representations and coding technologies that can improve the efficiency of visual data compression while addressing the computational challenges associated with AI-based approaches. The project provides a strong academic–industrial research environment, combining Inria's expertise in information theory, signal and image processing, and machine learning with InterDigital's expertise in media technologies and standardization.

Mission confiée

One of the factors responsible for the growing popularity of Implicit Neural Representation (INR) models for image compression is their relatively low decoding time and their compression efficiency [1]. INR-based image compression introduces a new paradigm in which each image is represented by a function that can be transmitted and subsequently used to reconstruct the image [2]. Once this function has been learned at the encoder and transmitted, the image can be reconstructed from the learned representation.

However, one of the main challenges of this paradigm is the long image encoding time. This is because learning the personalized function requires “overfitting” a separate model to each individual image or video, which can be computationally expensive and time-consuming [3]. This remains one of the main limitations to the practical deployment of INR-based compression methods [4].

Principales activités

In this project, we aim to investigate whether common knowledge can be extracted from the functions learned for individual images, thereby reducing the need to learn each function from scratch. More specifically, we seek to answer the following question: Can common knowledge be extracted from image-specific functions in order to reduce the training required for each new image?

To address this question, we will analyze learned INR functions and investigate whether some form of universality across images can be identified. Based on these insights, we will explore strategies for reducing the training complexity of INR-based architectures and, consequently, their image encoding time. The expected outcome is to develop INR-based compression models that retain the advantages of implicit representations while significantly reducing their computational cost at the encoder

Objectives

The main objectives of this project are:

• To investigate how INR-based models relate to conventional autoencoder architectures for image compression, and whether they exhibit common characteristics;
• To determine whether a subset of internal features or representations can be identified across different images;
• To exploit the universality of these features to develop lower-complexity INR-based image compression models with faster encoding times.

References

[1] T. Ladune, P. Philippe, F. Henry, G. Clare, and T. Leguay, “Cool-chic: Coordinate-based low complexity hierarchical image codec,” in International Conference on Computer Vision (ICCV), 2023, pp. 13 515–13 522.

[2] E. Dupont, A. Goliński, M. Alizadeh, Y. W. Teh, and A. Doucet, “COIN: Compression with implicit neural representations,” 2021. [Online]. Available: https://arxiv.org/abs/2103.03123

[3] E. Dupont, H. Loya, M. Alizadeh, A. Golinski, Y. W. Teh, and A. Doucet, “COIN++: Neural compression across modalities,” Transactions on Machine Learning Research, 2022. [Online]. Available: https://openreview.net/forum?id=NXB0rEM2Tq

[4] M. Gwilliam, R. Zhang, N. Padmanabhan, H. Du, and A. Shrivastava, “How to design and train your implicit neural representation for video compression,” in Winter Conference on Applications of Computer Vision (WACV), 2026.

Avantages

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Rémunération

Monthly gross salary from 2 788 euros.

Informations générales

• Thème/Domaine : Vision, perception et interprétation multimedia

  Systèmes d'information (BAP E)

• Ville : Rennes

• Centre Inria : Centre Inria de l'Université de Rennes

• Date de prise de fonction souhaitée : 2027-02-01

• Durée de contrat : 1 an, 6 mois

• Date limite pour postuler : 2026-11-29

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

Please submit online : your resume, cover letter and letters of recommendation eventually

Sécurité défense :
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :

Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

• Équipe Inria : COMPACT

• Recruteur :

  Roumy Aline / [email protected]

A propos d'Inria

Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l’État pour les stratégies nationales de recherche et d’innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d’innovation avec ses 3500 scientifiques, ingénieurs et personnels d’appui, en partenariat avec les universités et l’écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l’informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l’impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.