PivotCompress: Compression by Sorting

TL;DR

PivotCompress leverages quicksort decisions to efficiently encode and compress data by exploiting sorted permutations, achieving near-optimal rates for stationary sources and surpassing entropy bounds for non-uniform data.

Contribution

This work introduces a universal compression scheme based on quicksort decision encoding, which adapts to data distribution and improves compression for non-uniform data.

Findings

Nearly optimal compression rate for stationary sources

Can encode data below entropy bounds for non-uniform strings

Sparse comparison vectors enable better compression for highly non-uniform data

Abstract

Sorted data is usually easier to compress than unsorted permutations of the same data. This motivates a simple compression scheme: specify the sorted permutation of the data along with a representation of the sorted data compressed recursively. The sorted permutation can be specified by recording the decisions made by quicksort. If the size of the data is known, then the quicksort decisions describe the data at a rate that is nearly as efficient as the minimal prefix-free code for the distribution, which is bounded by the entropy of the distribution. This is possible even though the distribution is unknown ahead of time. Used in this way, quicksort acts as a universal code in that it is asymptotically optimal for any stationary source. The Shannon entropy is a lower bound when describing stochastic, independent symbols. However, it is possible to encode non-uniform, finite strings below the entropy of the sample distribution by also encoding symbol counts because the values in the sequence are no longer independent once the counts are known. The key insight is that sparse quicksort comparison vectors can also be compressed to achieve an even lower rate when data is highly non-uniform while incurring only a modest penalty when data is random.