Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy

Compression of remote sensing images is beneficial to both storage and transmission. For lossless compression, the upper and lower limits of compression ratio are defined by Shannon's source coding theorem with Shannon entropy as the metric, which measures the statistical information of a...

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Autores principales:	Xinghua Cheng, Zhilin Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Compression ratio configurational information empirical model for predicting compression ratios image coding Shannon's source coding theorem Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/e36d0be75eea4de8bdae9c21fedbb538
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id	oai:doaj.org-article:e36d0be75eea4de8bdae9c21fedbb538
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spelling	oai:doaj.org-article:e36d0be75eea4de8bdae9c21fedbb5382021-12-04T00:00:09ZPredicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy2151-153510.1109/JSTARS.2021.3123650https://doaj.org/article/e36d0be75eea4de8bdae9c21fedbb5382021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594713/https://doaj.org/toc/2151-1535Compression of remote sensing images is beneficial to both storage and transmission. For lossless compression, the upper and lower limits of compression ratio are defined by Shannon's source coding theorem with Shannon entropy as the metric, which measures the statistical information of a dataset. However, the calculation of the actual Shannon entropy of a large image is not an easy task, which limits the practicality of predicting the lossless compression ratio with Shannon entropy. On the other hand, most recently developed compression techniques take into consideration the configurational information of images to achieve a high compression ratio. This leads us to hypothesize that a metric capturing configurational information can be employed to build mathematical models for predicting compression ratios. To test this hypothesis, a two-step investigation was carried out, i.e., to find the most suitable metric through extensive experimental tests and to build a model upon this metric. A total of 1850 8-b images with 15 compression techniques were used to form the experimental dataset. First, 29 metrics were analyzed in terms of correlation magnitude, distinctiveness, and model contribution. As a result, the configurational entropy outperformed the rest. Second, six configurational entropy-based prediction models for predicting the compression ratio were established and tested. Results illustrated that these models work well. The PolyRatio model with 9.0 as a numerator, which was in a similar form to Shannon's theorem, performed best and was thus recommended. This article provides a new direction for building a theoretical prediction model with configurational entropy.Xinghua ChengZhilin LiIEEEarticleCompression ratioconfigurational informationempirical model for predicting compression ratiosimage codingShannon's source coding theoremOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11936-11953 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Compression ratio configurational information empirical model for predicting compression ratios image coding Shannon's source coding theorem Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Compression ratio configurational information empirical model for predicting compression ratios image coding Shannon's source coding theorem Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Xinghua Cheng Zhilin Li Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy
description	Compression of remote sensing images is beneficial to both storage and transmission. For lossless compression, the upper and lower limits of compression ratio are defined by Shannon's source coding theorem with Shannon entropy as the metric, which measures the statistical information of a dataset. However, the calculation of the actual Shannon entropy of a large image is not an easy task, which limits the practicality of predicting the lossless compression ratio with Shannon entropy. On the other hand, most recently developed compression techniques take into consideration the configurational information of images to achieve a high compression ratio. This leads us to hypothesize that a metric capturing configurational information can be employed to build mathematical models for predicting compression ratios. To test this hypothesis, a two-step investigation was carried out, i.e., to find the most suitable metric through extensive experimental tests and to build a model upon this metric. A total of 1850 8-b images with 15 compression techniques were used to form the experimental dataset. First, 29 metrics were analyzed in terms of correlation magnitude, distinctiveness, and model contribution. As a result, the configurational entropy outperformed the rest. Second, six configurational entropy-based prediction models for predicting the compression ratio were established and tested. Results illustrated that these models work well. The PolyRatio model with 9.0 as a numerator, which was in a similar form to Shannon's theorem, performed best and was thus recommended. This article provides a new direction for building a theoretical prediction model with configurational entropy.
format	article
author	Xinghua Cheng Zhilin Li
author_facet	Xinghua Cheng Zhilin Li
author_sort	Xinghua Cheng
title	Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy
title_short	Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy
title_full	Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy
title_fullStr	Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy
title_full_unstemmed	Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy
title_sort	predicting the lossless compression ratio of remote sensing images with configurational entropy
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e36d0be75eea4de8bdae9c21fedbb538
work_keys_str_mv	AT xinghuacheng predictingthelosslesscompressionratioofremotesensingimageswithconfigurationalentropy AT zhilinli predictingthelosslesscompressionratioofremotesensingimageswithconfigurationalentropy
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Predicting the Lossless Compression Ratio of Remote Sensing Images With Configurational Entropy

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