Framework One-Bit Quantization

Optimization-Based One-Bit Quantization — Framework, Code and Test Data

Reference implementation and reproducible test framework for optimization-based one-bit quantization, developed alongside the publications of F. Mayer and C. Vogel.

One-bit quantization converts real-valued signals into binary sequences, which simplifies hardware considerably but introduces substantial quantization noise. Sigma-Delta quantization shapes that noise out of the signal band, but is essentially limited to lowpass signals. This framework formulates the conversion as a constrained optimization problem instead: the binary sequence is chosen to minimize a filter-weighted error norm, so the choice of the FIR filter determines where the quantization noise ends up — lowpass, bandpass or multiband targets are handled by the same algorithm. Solving the problem block by block keeps it tractable for long signals.

The archive contains the quantization algorithms, a parametrized generator for the multitone test scenarios, the evaluation and plotting tools, and the corresponding parameter sets.

Getting started: unpack the archive and open README.md for an overview of the repository structure and the individual modules. HOWTO.md is the step-by-step guide: required Python packages, the Gurobi academic license needed for the solver, and the three-stage pipeline from signal generation through quantization to evaluation, including how to define your own test scenarios.

Requires Python 3 with NumPy, SciPy, Matplotlib and PyQt5, plus a Gurobi license (free for academic use).

Funded by the Austrian Science Fund (FWF) [10.55776/DFH 5] within the DENISE project and the province of Styria.