Niels Jeppesen | Searcharxiv

arXiv2025

Pcodec: Better Compression for Numerical Sequences

We present Pcodec (Pco), a format and algorithm for losslessly compressing numerical (float or integer) sequences. Pco's core and most novel component is a binning algorithm that quickly converges to the true entropy of smoothly, independently, and identically distributed (SIID) integers. We mathematically prove this convergence with a practical bound. To accommodate data this is not SIID, Pco has two opinionated preprocessing steps. The first step, Pco's mode, decomposes the numbers into more smoothly distributed integer latent variables. The second step, delta encoding, makes the latents more independently and identically distributed. We demonstrate that Pco achieves 29-94% higher compression ratio than other numerical codecs on six real-world columnar datasets while using less compression time.

cs.IT↗

arXiv2022

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.

cs.CV↗