publications

2023

1. 

   Implicit Neural Multiple Description for DNA-based data storage

   Trung Hieu Le, Xavier Pic, Jeremy Mateos, and 1 more author

   In Arxiv preprint arXiv:2309.06956, 2023

   Abs arXiv

   DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.

2. 

   INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for robust image Coding

   Trung Hieu Le, Xavier Pic, and Marc Antonini

   In IEEE International Workshop on MultiMedia Signal Processing (MMSP 2023), 2023

   Abs arXiv

   Multiple Description Coding (MDC) is an error-resilient source coding method designed for transmission over noisy channels. We present a novel MDC scheme employing a neural network based on implicit neural representation. This involves overfitting the neural representation for images. Each description is transmitted along with model parameters and its respective latent spaces. Our method has advantages over traditional MDC that utilizes auto-encoders, such as eliminating the need for model training and offering high flexibility in redundancy adjustment. Experiments demonstrate that our solution is competitive with autoencoder-based MDC and classic MDC based on HEVC, delivering superior visual quality.

3. 

   Multiple Description Video Coding for Real-Time Applications using HEVC

   Trung Hieu Le, Marc Antonini, Marc Lambert, and 1 more author

   In 2023 IEEE International Conference on Image Processing (ICIP), 2023

   Abs arXiv

   Remote control vehicles require the transmission of large amounts of data, and video is one of the most important sources for the driver. To ensure reliable video transmission, the encoded video stream is transmitted simultaneously over multiple channels. However, this solution incurs a high transmission cost. To address this issue, it is necessary to use more efficient video encoding methods that can make the video stream robust to noise. Moreover it should have a less complexity to adapt to the real time requirement. In this paper, we propose a low-complexity, low-latency 2-channel Multiple Description Coding (MDC) solution with an adaptive Instantaneous Decoder Refresh (IDR) frame period, which is compatible with the HEVC standard with adaptive redundancy adjustment. This method shows a better resistance to high packet loss rates with lower complexity.

2022

1. Codage vidéo à description multiple basé sur HEVC pour le pilotage de véhicules semi-autonomes

   Trung Hieu Le, Marc Antonini, Marc Lambert, and 1 more author

   In GRETSI, 2022

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