“Neural image compression in a nutshell” by Dr Luis Herranz
Neural image compression is a new paradigm where codecs are modeled as deep neural networks whose parameters are learned from data. There has been increasing interest in this paradigm as a possible competitor to traditional image coding methods based on block-based transform coding.
If you are interested in learning more about neural image compression, do not hesitate to read these articles by our researcher Dr Luis Herranz.
Neural image compression in a nutshell:
- Part 1| Main idea: http://www.lherranz.org/2022/08/24/neural-image-compression-in-a-nutshell-part-1-main-idea/
- Part 2 | Architectures and comparison: http://www.lherranz.org/2022/08/31/neural-image-compression-in-a-nutshell-part-2-architectures-and-comparison/
And, if you want to delve more into the topic, we encourage you to read:
- MAE, SlimCAE and DANICE. Towards practical neural image compression: https://www.lherranz.org/2022/09/17/mae-slimcae-and-danice-towards-practical-neural-image-compression/