Rate-distortion: Blahut-Arimoto vs. gradient descent vs. mapping approach

This repository contains implementations (using tensorflow v2) of 4 different algorithms to determine the optimal Shannon rate-distortion trade-off for a given set of samples from a continuous source,

• RD_BA.py: Blahut-Arimoto algorithm.
• RD_GD.py: Direct optimization using gradient descent.
• RD_MA.py: Two different versions (direct and iterative optimization) of the mapping approach by Rose.

View the notebook (using nbviewer) for an explanation of each algorithm.

Note: For a combination of the Blahut-Arimoto algorithm and the mapping approach, resulting in a more efficient algorithm, see RDFC (rate-distortion-with-fixed-cardinality).