Lossy Compressive Sensing Based on Online Dictionary Learning


  • İrem Ülkü Department of Electrical and Electronics Engineering, Çankaya University, Ankara
  • Ersin Kizgut Instituto Universitario de Matemática Pura y Applicada (IUMPA), Universitat Politècnica de València


Hyperspectral imaging, compression algorithms, dictionary learning, sparse coding


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.


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How to Cite

Ülkü, İrem, & Kizgut, E. (2019). Lossy Compressive Sensing Based on Online Dictionary Learning. COMPUTING AND INFORMATICS, 38(1), 151–172. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/2019_1_151