# Optimal quantization for piecewise uniform distributions

## TL;DR

This paper develops a general method for optimal quantization of distributions and applies it to piecewise uniform distributions, providing explicit solutions for both finite and infinite piece cases.

## Contribution

It introduces a unified approach to quantization using ergodic maps and applies it to derive explicit optimal sets for piecewise uniform distributions.

## Key findings

- Explicit optimal sets of n-means for finite pieces
- Asymptotic optimal quantization errors for all n
- Difference in approach between finite and infinite piece distributions

## Abstract

Quantization for a probability distribution refers to the idea of estimating a given probability by a discrete probability supported by a finite number of points. In this paper, firstly a general approach to this process is outlined using independent random variables and ergodic maps; these give asymptotically the optimal sets of $n$-means and the $n$th quantization errors for all positive integers $n$. Secondly two piecewise uniform distributions are considered on $\mathbb R$: one with infinite number of pieces and one with finite number of pieces. For these two probability measures, we describe the optimal sets of $n$-means and the $n$th quantization errors for all $n\in \mathbb N$. It is seen that for a uniform distribution with infinite number of pieces to determine the optimal sets of $n$-means for $n\geq 2$ one needs to know an optimal set of $(n-1)$-means, but for a uniform distribution with finite number of pieces one can directly determine the optimal sets of $n$-means and the $n$th quantization errors for all $n\in \mathbb N$.