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Subject: Improvement of end-to-end learned video coding framework
The COVID-19 crisis once more showed how dependent are our societies on digital video broadcast. An industry which is itself highly restricted by the bandwidth for distribution of a tremendous amount of video over different transportation means (Terrestrial, Satellite, Internet, mobile). This increase in video consumption leads to a massive usage of computational resources in datacenters. This has two principal impacts. First, the encoding costs for service providers and broadcasters grow, since they’re often charged on a CPU/Bandwidth-usage basis. Second, the increase in energy consumption and its impact on the environment are more important. More than ever, it is necessary to improve video compression efficiency.
In the past few years, end-to-end learned image and video coding methods have shown promising results, making them compete with traditional video codecs such as High Efficiency Video Coding (HEVC) or Versatile Video Coding (VVC), in terms of compression efficiency. In this context, we implemented a software framework for end-to-end learned video coding adapted to practical application. The goal of this internship is to improve this framework in order to make it modular, configurable and generalizable to several end-to-end learned codecs.
The position is based in Rennes (35), teleworking possible
1500€ gross + Ticket restaurant + Reimbursement of transportation fees + possibility of recruitment (CDI) or CIFRE thesis
This internship will consist of following phases:
- State-of-the art of end-to-end learned image and video codecs.
- Introduction, understanding, and operating with the existing version of the framework,
- Improve the implementation of the framework by making the software modular and configurable.
- Generalize the framework to make it compatible with other end-to-end learned codecs
In all above phases, candidate will constantly interact with his/her colleagues to benefit from their knowledge and experience. Noteworthy of precising that, based on the profile of candidate, the internship orientation might be adjusted.
About the candidate:
- Last-year student of engineering school program or master’s in computer science or electrical engineering.
- Excellent python programming skills.
- Hand-on experience with neural networks and libraries such as TensorFlow, PyTorch etc.
- Ability to read and understand academic papers in the domain of image and video processing.
- Being familiar with video compression algorithms is a plus.
- Fluent in English.
Equal Employment Opportunity:
Ateme SA and all its subsidiaries is an Equal Opportunity. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.