Data compression on the sphere

TL;DR

This paper presents algorithms for compressing data on the sphere, including wavelet transforms and encoding methods, achieving significant size reduction with minimal information loss for applications like CMB analysis and geographic data.

Contribution

It introduces a novel spherical data compression framework combining wavelet transforms with Huffman and run-length encoding, applicable to various spherical datasets.

Findings

CMB data compressed to 40% with no information loss

Topography and illumination data compressed to 1/40th size with slight quality loss

The SZIP program implementing these algorithms is publicly available

Abstract

Large data-sets defined on the sphere arise in many fields. In particular, recent and forthcoming observations of the anisotropies of the cosmic microwave background (CMB) made on the celestial sphere contain approximately three and fifty mega-pixels respectively. The compression of such data is therefore becoming increasingly important. We develop algorithms to compress data defined on the sphere. A Haar wavelet transform on the sphere is used as an energy compression stage to reduce the entropy of the data, followed by Huffman and run-length encoding stages. Lossless and lossy compression algorithms are developed. We evaluate compression performance on simulated CMB data, Earth topography data and environmental illumination maps used in computer graphics. The CMB data can be compressed to approximately 40% of its original size for essentially no loss to the cosmological information content of the data, and to approximately 20% if a small cosmological information loss is tolerated. For the topographic and illumination data compression ratios of approximately 40:1 can be achieved when a small degradation in quality is allowed. We make our SZIP program that implements these compression algorithms available publicly.