A Systematic Evaluation of Coding Strategies for Sparse Binary Images

TL;DR

This paper systematically evaluates coding strategies for sparse binary images in inpainting-based compression, comparing existing methods and developing improved codecs through component analysis and combination.

Contribution

It provides the first comprehensive analysis of coding strategies for sparse binary images, introducing new codecs with enhanced efficiency and speed-performance trade-offs.

Findings

Identified key components that improve compression efficiency.

Compared various existing methods in terms of efficiency and runtime.

Developed new codecs with better compression ratios or faster performance.

Abstract

Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation of this problem so far. To this end, we first review and compare a wide range of existing methods from image compression and general purpose coding in terms of their coding efficiency and runtime. Afterwards, an ablation study enables us to identify and isolate the most useful components of existing methods. With context mixing, we combine those ingredients into new codecs that offer either better compression ratios or a more favourable trade-off between speed and performance.