Damjan Strnad (Author), Danijel Žlaus (Author), Andrej Nerat (Author), Borut Žalik (Author)

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

Keywords

strojno učenje;verižne kode;binarno enkodiranje;binary mask;machine learning;chain code;binary encoding;bitmap reconstruction;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher: Springer Nature
UDC: 004.42
COBISS: 207446531 Link will open in a new window
ISSN: 1573-7721
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Downloads: 1
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Other data

Secondary language: Slovenian
Secondary keywords: strojno učenje;verižne kode;binarno enkodiranje;
Type (COBISS): Article
Pages: 19 str.
Chronology: Published: 09 September 2024
DOI: 10.1007/s11042-024-20199-7
ID: 25801683