| Title: | Efficient compressed storage and fast reconstruction of large binary images using chain codes |
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| Authors: | ID Strnad, Damjan (Author)
ID Žlaus, Danijel (Author)
ID Nerat, Andrej (Author)
ID Žalik, Borut (Author) |
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| Files: |  s11042-024-20199-7_(1).pdf (1,45 MB)
MD5: 2D27E2A7F4859AB6D320CE2CE2808F2D
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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 |
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| Keywords: | binary mask, machine learning, chain code, binary encoding, bitmap reconstruction |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 02.07.2024 |
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| Article acceptance date: | 30.08.2024 |
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| Publication date: | 09.09.2024 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2024 |
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| Number of pages: | 19 str. |
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| PID: | 20.500.12556/DKUM-91712  |
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| UDC: | 004.42 |
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| ISSN on article: | 1573-7721 |
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| COBISS.SI-ID: | 207446531 |
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| DOI: | 10.1007/s11042-024-20199-7 |
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| Copyright: | © The Author(s) 2024 |
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| Publication date in DKUM: | 29.01.2025 |
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| Views: | 171 |
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| Downloads: | 157 |
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| Metadata: |    |
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| Categories: | Misc.
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