Abstract
Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.
Original language | American English |
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Article number | 8858042 |
Pages (from-to) | 790-798 |
Number of pages | 9 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 58 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2020 |
Keywords
- Lossless and near-lossless compression
- pyramidal multiresolution scheme
- regression wavelet analysis (RWA)
- remote sensing data compression