Atomic wavelets in lossy and near-lossless image compression

Victor O. Makarichev; Vladimir V. Lukin; Iryna V. Brysina; Benoit Vozel; Kacem Chehdi

doi:10.1117/12.2573970

20 September 2020 Atomic wavelets in lossy and near-lossless image compression

Victor O. Makarichev, Vladimir V. Lukin, Iryna V. Brysina, Benoit Vozel, Kacem Chehdi

Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI; 1153313 (2020) https://doi.org/10.1117/12.2573970
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

One of distinguishing features of the present is the explosive increase in data amount including digital images such as satellite remote sensing images. Processing, storing and transmission via networks of a huge number of digital images requires considerable resources in the sense of memory, time, computational power, etc. In this regard, the importance of image compression is growing and the development of novel compression techniques that would satisfy new requirements, for instance, to security and level of privacy protection continues. Atomic wavelets and their generalizations can be a useful tool for this. They are constructed using atomic functions, which are compactly supported solutions of special functional differential equations. Discrete atomic transform (DAT), which is a process of computation of expansion coefficients of the function describing the source discrete data, is applied in discrete atomic compression (DAC). DAC can be applied to compression of full color digital images, as well as monocomponent ones. In this paper, we investigate efficiency, which is measured by compression ratio (CR), of satellite image compression using DAC and the corresponding loss of quality measured by several quantitative criteria. They are maximum absolute deviation (MAD), root mean square (RMS) and peak signal-to-noise ratio (PSNR). We show that DAC provides near lossless compression, when quality loss is minor in a sense of the MAD-metric. Also, it is proved that using DAC it is possible to obtain better compression than by applying JPEG with the same quality of the obtained results.

Conference Presentation

Citation Download Citation

Victor O. Makarichev, Vladimir V. Lukin, Iryna V. Brysina, Benoit Vozel, and Kacem Chehdi "Atomic wavelets in lossy and near-lossless image compression", Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 1153313 (20 September 2020); https://doi.org/10.1117/12.2573970

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available