Abstract
In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.
Keywords
MSC
68U10, 94A08
Cite this article
@article{cai-4225,
author = {İrem Ülkü and Ersin Kizgut},
title = {Lossy Compressive Sensing Based on Online Dictionary Learning},
journal = {Computing and Informatics},
volume = {38},
number = {1},
pages = {151--172},
year = {2019},
issn = {1335-9150},
doi = {10.31577/cai_2019_1_151}
}
TY - JOUR AU - İrem Ülkü AU - Ersin Kizgut TI - Lossy Compressive Sensing Based on Online Dictionary Learning JO - Computing and Informatics VL - 38 IS - 1 SP - 151 EP - 172 PY - 2019 SN - 1335-9150 DO - 10.31577/cai_2019_1_151 UR - https://doi.org/10.31577/cai_2019_1_151 KW - Hyperspectral imaging KW - compression algorithms KW - dictionary learning KW - sparse coding ER -
Ülkü, İ., & Kizgut, E. (2019). Lossy Compressive Sensing Based on Online Dictionary Learning. Computing and Informatics, 38(1), 151–172. https://doi.org/10.31577/cai_2019_1_151