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Title: | How does Shannon's source coding theorem fare in prediction of image compression ratio with current algorithms? | Authors: | Cheng, X Li, Z |
Issue Date: | 2020 | Source: | International archives of the photogrammetry, remote sensing and spatial information sciences, 2020, v. 43, no. B3, p. 1313-1319 | Abstract: | Images with large volumes are generated daily with the advent of advanced sensors and platforms (e.g., satellite, unmanned autonomous vehicle) of data acquisition. This incurs issues on the storage, processing, and transmission of images. To address such issues, image compression is essential and can be achieved by lossy and/or lossless approaches. With lossy compression, a high compression ratio can usually be achieved but the original data can never be completely recovered. On the other hand, with lossless compression, the original information is well reserved. Lossless compression is very desirable in many applications such as remote sensing, geological surveying. Shannon's source coding theorem has defined the theoretical limits of compression ratio. However, some researchers have discovered that some compression techniques have achieved a compression ratio that is higher than the theoretical limits. Then, two questions naturally arise, i.e., "When this happens?" and "Why this happens?". This study is dedicated to giving answers to these two questions. Six algorithms are used to compress 1650 images with different complexities. The experimental results show that the generally acknowledged Shannon's coding theorem is still good enough for predicting compression ratio by the algorithms with consideration of statistical information only, but not capable of predicting compression ratio by the algorithms with consideration of configurational information of pixels. Overall, this study indicates that new empirical (or theoretical) models for predicting lossless compression ratio can be built with metrics capturing configurational information. | Keywords: | Coding theorem Compression ratio Image quality Information theory Shannon entropy |
Publisher: | Copernicus GmbH | Journal: | International archives of the photogrammetry, remote sensing and spatial information sciences | ISSN: | 1682-1750 | EISSN: | 2194-9034 | DOI: | 10.5194/isprs-archives-XLIII-B3-2020-1313-2020 | Description: | 2020 24th ISPRS Congress - Technical Commission III, 31 August - 2 September 2020 | Rights: | © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/). The following publication Cheng, X. and Li, Z.: HOW DOES SHANNON’S SOURCE CODING THEOREM FARE IN PREDICTION OF IMAGE COMPRESSION RATIO WITH CURRENT ALGORITHMS?, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1313–1319, is available at https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1313-2020, 2020 |
Appears in Collections: | Conference Paper |
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Cheng_How_does_shannon.pdf | 1.38 MB | Adobe PDF | View/Open |
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