Post-doctoral researcher; Hybrid Implicit Neural Representations for Next-Generation Learned Video Compression at InterDigital in Rennes

Job description

Join InterDigital's Video Lab to pioneer the next generation of energy-efficient AI for video. As a postdoctoral researcher, you will help shape technologies that reduce the environmental footprint of generative AI while contributing to international standards and publishing cutting-edge research. 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 . Background and Motivation The continuous growth of video traffic generated by streaming platforms, immersive media, cloud gaming, autonomous systems, and machine vision is placing unprecedented pressure on communication and storage infrastructures. Although conventional video codecs such as H.266/VVC, AV1 and HEVC continue to improve coding efficiency through decades of algorithmic refinements, their block-based architecture is approaching diminishing returns. Their design relies on handcrafted signal processing modules — including prediction, transforms, quantization, in-loop filtering and entropy coding — that have been carefully optimized but remain fundamentally constrained by assumptions about natural image statistics. Learned Video Compression (LVC) has emerged during the past decade as a promising alternative. Instead of optimizing individual codec modules independently, learned codecs formulate compression as a global end-to-end optimization problem. Neural networks jointly learn the analysis transform, synthesis transform, entropy model and motion representation by minimizing a rate-distortion objective where (D) measures reconstruction distortion while (R) estimates the coding rate through differentiable entropy models. Recent learned image and video codecs have demonstrated compression performances comparable to — or surpassing — the latest standard codecs for several operating points. More importantly, they provide a flexible framework in which every component can be jointly optimized for a target application. Despite these impressive advances, several important challenges remain before learned video codecs can become a practical replacement for conventional standards. First, current architectures remain computationally demanding. Deep convolutional encoders, decoders and optical-flow estimation networks require millions of parameters and substantial computational resources during both training and inference. Second, entropy modeling remains one of the principal bottlenecks. State-of-the-art autoregressive models improve coding efficiency but introduce strong sequential dependencies that significantly reduce decoding parallelism. Third, current codecs still represent images and videos through discrete latent feature maps. While highly effective, these representations inherit many limitations of grid-based signals, including resolution dependence, interpolation artifacts and limited geometric adaptability. Finally, temporal redundancy is ofte