Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution.

Shaoming Pan; XiaoLin Gu; Yanwen Chong; Yuanyuan Guo

doi:10.9781/ijimai.2022.08.004

Authors

Shaoming Pan Wuhan University
XiaoLin Gu Wuhan University
Yanwen Chong Wuhan University
Yuanyuan Guo Wuhan University

DOI:

https://doi.org/10.9781/ijimai.2022.08.004

Keywords:

Compression, Dynamic Strategy, Maps, Hyperspectral Image, Multi-Depth

Supporting Agencies

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62072345, 41671382), State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing Special Research Funding. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

Abstract

In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset.

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