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Non-blind post-processing algorithm for remote sensing image compression

delete2021-02-01
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PRE
AI
J
Jin Li *
Y
Yanyan Liu
DOI:10.1016/j.knosys.2020.106719delete
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Abstract

Abstract

En 中文
High-efficiency compression of remote sensing images (RSIs) is very necessary after images are acquired because the on-orbit transmission bandwidth and memory capacity are limited. Wavelet-based compression methods have been widely used in on-orbit image compressors for optical cameras. However, wavelet transforms have low sparse representation capability for edges (i.e., high-frequency information) in RSIs. A lot of wavelet coefficients of edges have high magnitude because spatial redundancies between these coefficients still exist, which is not suitable for the subsequent compression. In this paper, we propose a non-blind post-processing approach in the wavelet domain. The non-blind post-processing uses a high-frequency detection algorithm to establish a high-frequency map, which is used to directly guide the allocation of post-transform resources (e.g., multi-basis dictionary post transform and the rate-distortion estimation). Post-transform resources can be allocated to high-frequency areas but not to low-frequency areas because the smooth areas need not be performed by the post-processing, while detailed areas need more post-processing resources. The best transform estimators are only performed to determine the best transform at the high-frequency areas, while need not at low-frequency areas. The proposed method can improve the post-processing efficiency and compression performance because the post-transform exploits the redundancies among wavelet coefficients and removes large-amplitude coefficients of high-frequency areas in the wavelet domain. The proposed method is confirmed and experimental results demonstrate that the proposed method obtains a high calculation efficiency and high compression performance compared with the blind post-processing. The proposed method is suitable for the compression of RSIs and other images. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Blind post-processing
Non-blind post-processing
Remote sensing image
Compression
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Journal

K
Knowledge-Based Systems
IF:
7.6
Papers:
1.2W
Citations:
4.5W

Organization

U
University of Cambridge
Scholars:
7.7W
Papers: 7.1W
Citations: 13.7W
C
changchun university of science & technology
Scholars:
6.7K
Papers: 4.2K
Citations: 3