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초록
Hyperspectral images are of very large data size and highly correlated in neighboring bands, therefore, it is necessary to realize the efficient compression performance on the condition of low encoding complexity. In this paper, we propose a method based on both partitioning embedded block and lossless adaptive-distributed arithmetic coding (LADAC). Combined with three-dimensional wavelet transform and SW-SPECK algorithm, LADAC is adopted according to the correlation between the adjacent bit-plane. Experimental results show that our proposed algorithm outperforms 3D-SPECK, furthermore, our method need not take the inter-band prediction or transform into account, so the complexity is small relatively.
키워드
Arithmetic coding; Distributed arithmetic coding; Distributed source coding; Hyperspectral imagery; LDPC codes; Lossless adaptive-distributed arithmetic coding; Slepian-wolf coding; Arithmetic Coding; Distributed arithmetic; Distributed source coding; Hyperspectral imagery; LDPC codes; Lossless; Slepian-Wolf coding; Data compression; Data processing; Error correction; Filter banks; Image compression; Independent component analysis; Remote sensing; Three dimensional; Wavelet transforms; Image coding
- 제목
- Hyperspectral image compression using distributed arithmetic coding and bit-plane coding
- 저자
- Wu, Jiaji; Wang, Minli; Fang, Yong; Jeong, Jechang; Jiao, Licheng
- 발행일
- 2010-08
- 유형
- Conference Paper
- 저널명
- Proceedings of SPIE - The International Society for Optical Engineering
- 권
- 7810
- 페이지
- 1 ~ 8