High-Quality and Low-Memory-Footprint Progressive Decoding of Large-Scale Particle Data

Particle representations are used often in large-scale simulations and observations, frequently creating datasets containing several millions of particles or more. Due to their sheer size, such datasets are difficult to store, transfer, and analyze efficiently. Data compression is a promising solution; however, effective approaches to compress particle data are lacking and no community-standard and accepted techniques exist. Current techniques are designed either to compress small data very well but require high computational resources when applied to large data, or to work with large data but without a focus on compression, resulting in low reconstruction quality per bit stored. In this paper, we present innovations targeting tree-based particle compression approaches that improve the tradeoff between high quality and low memory-footprint for compression and decompression of large particle datasets. Inspired by the lazy wavelet transform, we introduce a new way of partitioning space, which allows a low-cost depth-first traversal of a particle hierarchy to cover the space broadly. We also devise novel data-adaptive traversal orders that significantly reduce reconstruction error compared to traditional data-agnostic orders such as breadth-first and depth-first traversals. The new partitioning and traversal schemes are used to build novel particle hierarchies that can be traversed with asymptotically constant memory footprint while incurring low reconstruction error. Our solution to encoding and (lossy) decoding of large particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error heuristics can be supplied by the user. Finally, through extensive experimentation, we demonstrate the efficacy and the flexibility of the proposed techniques when combined as well as when used independently with existing approaches on a wide range of scientific particle datasets.