20.500.12556/DKUM-91712 Efficient compressed storage and fast reconstruction of large binary images using chain codes Large binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between and faster reconstruction on test data binary mask machine learning chain code binary encoding bitmap reconstruction strojno učenje verižne kode binarno enkodiranje true true false Springer Nature Angleški jezik Slovenski jezik https://link.springer.com/article/10.1007/s11042-024-20199-7 © The Author(s) 2024 Članek v reviji 2025-01-29 16:10:44 2025-01-29 16:10:44 2025-01-30 03:11:23 0000-00-00 00:00:00 2024 0 0 19 str. Published: 09 September 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-07-02 2024-08-30 2024-09-09 004.42 207446531 10.1007/s11042-024-20199-7 1573-7721 s11042-024-20199-7_(1).pdf s11042-024-20199-7_(1).pdf 1 2D27E2A7F4859AB6D320CE2CE2808F2D 5f0bfc83492cc2d1451647e81d3309699823f1340e778380ef33e0a542e8362b 758196dc-de53-11ef-9dfa-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188292 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0