End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are on par with the latest compression standard like VVC/H.266 on objective metrics, practical implementation and complexity remain strong issues for real-world applications. In this paper, we propose a practical implementation suitable for realistic applications, leading to a low-complexity model. We demonstrate that some gains can be achieved on top of a state-of-the-art low-complexity AE, even when using simpler implementation. Improvements include off-training entropy coding improvement and encoder side Rate Distortion Optimized Quantization. Results show a 19% improvement in BDrate on basic implementation of fully-factorized model, and 15.3% improvement compared to the original implementation. The proposed implementation also allows a direct integration of such approaches on a variety of platforms.
@article{arxiv.2303.05962,
title = {Entropy Coding Improvement for Low-complexity Compressive Auto-encoders},
author = {Franck Galpin and Muhammet Balcilar and Frédéric Lefebvre and Fabien Racapé and Pierre Hellier},
journal= {arXiv preprint arXiv:2303.05962},
year = {2023}
}