View in EDS

Efficient group based Hilbert encoding and decoding algorithms.

Saved in:
Bibliographic Details
Title: Efficient group based Hilbert encoding and decoding algorithms.
Authors: Jia, Lianyin1 (AUTHOR) lianyinjia@kust.edu.cn, Wang, Songyu1 (AUTHOR) wsongyu913@163.com, Sun, Shaowen1 (AUTHOR) qq1023401301@163.com, Ding, Jiaman1 (AUTHOR) jiamanding@kust.edu.cn, Li, Mengjuan2 (AUTHOR) lmjlykm@163.com, You, Jinguo1 (AUTHOR) jgyou@kust.edu.cn, Qiao, Shaojie3,4,5 (AUTHOR) sjqiao@cuit.edu.cn
Source: Pattern Recognition. Nov2025, Vol. 167, pN.PAG-N.PAG. 1p.
Subjects: Time complexity, Problem solving, Algorithms, Encoding
Abstract: Promoting Hilbert curve encoding and decoding efficiency is crucial for many Hilbert curve based applications. The current state-view based algorithms usually design 1-order state-views (1-SVs) and iteratively access these state-views in order-wise manner, leading to excessive state-view accesses. To solve this problem, this paper uses a new paradigm and designs novel k -order state-views (k -SVs), which coalesces k state-view accesses into one access, significantly decreasing the state-view accesses. An efficient grouping framework is then designed to divide each input data into groups. Efficient virtual filling mechanisms are further developed to facilitate the k -SVs to all the groups, thus improving the time and space efficiency. Based on these techniques, efficient grouping encoding and decoding algorithms and the corresponding batched algorithms are designed. Compared with the existing ones, our algorithms reduce the time complexity from O (n) to O (n / k) , where n is the total number of orders in each coordinate or a Hilbert code. Experimental results show that our algorithms run up to 3.18 times faster than the fastest competitor. • Novel k-order state-views merge k accesses into one, decreasing state-view accesses. • Virtual filling extends k-order views to all groups, boosting time/space efficiency. • Efficient algorithms achieve 3.18 × speedup over competitors. [ABSTRACT FROM AUTHOR]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Promoting Hilbert curve encoding and decoding efficiency is crucial for many Hilbert curve based applications. The current state-view based algorithms usually design 1-order state-views (1-SVs) and iteratively access these state-views in order-wise manner, leading to excessive state-view accesses. To solve this problem, this paper uses a new paradigm and designs novel k -order state-views (k -SVs), which coalesces k state-view accesses into one access, significantly decreasing the state-view accesses. An efficient grouping framework is then designed to divide each input data into groups. Efficient virtual filling mechanisms are further developed to facilitate the k -SVs to all the groups, thus improving the time and space efficiency. Based on these techniques, efficient grouping encoding and decoding algorithms and the corresponding batched algorithms are designed. Compared with the existing ones, our algorithms reduce the time complexity from O (n) to O (n / k) , where n is the total number of orders in each coordinate or a Hilbert code. Experimental results show that our algorithms run up to 3.18 times faster than the fastest competitor. • Novel k-order state-views merge k accesses into one, decreasing state-view accesses. • Virtual filling extends k-order views to all groups, boosting time/space efficiency. • Efficient algorithms achieve 3.18 × speedup over competitors. [ABSTRACT FROM AUTHOR]
ISSN:00313203
DOI:10.1016/j.patcog.2025.111718