Open AccessOptimization of computational complexity of lossy compression algorithms for hyperspectral images
Author(s) -
L. I. Lebedev,
Anastasia Shakhlan,
Russia Moscow
Publication year - 2019
Publication title -
ceur workshop proceedings
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.177
H-Index - 52
ISSN - 1613-0073
DOI - 10.18287/1613-0073-2019-2391-297-301
Subject(s) - lossy compression , hyperspectral imaging , computational complexity theory , computer science , algorithm , data compression , compression (physics) , image compression , pixel , artificial intelligence , image (mathematics) , similarity (geometry) , texture compression , pattern recognition (psychology) , computer vision , image processing , materials science , composite material
In this paper, we consider the solution of the problem of increasing the speed of the algorithm for hyperspectral images (HSI) compression, based on recognition methods. Two methods are proposed to reduce the computational complexity of a lossy compression algorithm. The first method is based on the use of compression results obtained with other parameters, including those of the recognition method. The second method is based on adaptive partitioning of hyperspectral image pixels into clusters and calculating the estimates of similarity only with the templates of one of the subsets. Theoretical and practical estimates of the increase in the speed of the compression algorithm are obtained.