International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1313–1319, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1313-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1313–1319, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1313-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Aug 2020

22 Aug 2020

HOW DOES SHANNON’S SOURCE CODING THEOREM FARE IN PREDICTION OF IMAGE COMPRESSION RATIO WITH CURRENT ALGORITHMS?

X. Cheng1 and Z. Li1,2 X. Cheng and Z. Li
  • 1Dept. of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
  • 2Faculty of Geosciences and Environmental Engineering & State-Province Joint Engineering Laboratory in Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu, China

Keywords: Coding theorem, Image quality, Information theory, Shannon entropy, Compression ratio

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.