version 0.7.0
- Generates coding and decoding matrices.
- Probabilistic decoding: Belief Propagation algorithm.
- Images transmission simulation (channel model: AGWN).
- Sound transmission simulation (channel model :AGWN).
Image coding-decoding example:
Sound coding-decoding example:
Sound Transmission
From pip:
$ pip install --upgrade pyldpc
Requiries: numpy, scipy, automatically installed with pip.
Jupyter notebooks:
Many changes in tutorials in v.0.7.0
- Users' Guide:
1- LDPC Coding-Decoding Simulation
2- Images Coding-DecodingTutorial
3- Sound Coding-DecodingTutorial
4- LDPC Matrices Construction Tutorial
- For LDPC construction details:
1- pyLDPC Construction(French)
2- LDPC Images Functions Construction
3- LDPC Sound Functions Construction
Contains:
- Coding and decoding matrices Generators:
- Regular parity-check matrix using Callager's method.
- Coding Matrix G both non-systematic and systematic.
- Coding function adding Additive White Gaussian Noise.
- Decoding functions using Probabilistic Decoding (Belief propagation algorithm):
- Default BP algorithm.
- Full-log BP algorithm.
- Images transmission sub-module:
- Coding and Decoding Grayscale and RGB Images.
- Pixel by pixel coding & decoding (small matrices)
- Row by row coding & decoding (large sparse matrices)
- BER: Bit Error Rate function.
- Sound transmission sub-module:
- Coding and Decoding audio files.
- BER_audio: Bit Error Rate function.
What's new:
- Compatibility of scipy.sparse.csr objects (CSR format) and numpy arrays.
- Row by row image decoding (More efficient than pixel coding) using large matrices.
- 4 times faster coding.
- 5 to 10 times faster decoding.
- Use of large matrices (csr) in sound transmission sub-module.
- Library of ready-to-use large matrices (csr).
- Text Transmission functions.
Please contact [email protected] for any bug encountered / any further information.