# Row-Centric Lossless Compression of Markov Images

## TL;DR

This paper compares row-centric lossless compression methods for Markov images, finding that simple 1-sided model-based coding outperforms Reduced Cutset Coding in low-order correlation scenarios, with conventional methods nearly matching its performance.

## Contribution

It provides a comparative analysis of row-centric coding strategies, highlighting the effectiveness of 1-sided model-based coding over RCC for low-order correlated Markov sources.

## Key findings

- 1-sided model-based coding outperforms RCC in low-order correlation sources
- Conventional context-based coding is nearly as effective as 1-sided model-based coding
- For low-order correlations, simple models offer better rate-complexity trade-offs

## Abstract

Motivated by the question of whether the recently introduced Reduced Cutset Coding (RCC) offers rate-complexity performance benefits over conventional context-based conditional coding for sources with two-dimensional Markov structure, this paper compares several row-centric coding strategies that vary in the amount of conditioning as well as whether a model or an empirical table is used in the encoding of blocks of rows. The conclusion is that, at least for sources exhibiting low-order correlations, 1-sided model-based conditional coding is superior to the method of RCC for a given constraint on complexity, and conventional context-based conditional coding is nearly as good as the 1-sided model-based coding.