1999 Fiscal Year Final Research Report Summary
Multispectral Image Compression Using Interband Correlation And Wavelet Transform
| Project/Area Number |
10650383
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報通信工学
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| Research Institution | KANAZAWA INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
TAKEBE Tsuyoshi Kanazawa Institute of Technology, Faculty of Technology, Professor, 工学部, 教授 (20019699)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUMURA Shuitsu Kanazawa Technical College, Professor, 電気工学科, 教授 (50064457)
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| Project Period (FY) |
1998 – 1999
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| Keywords | Multispectral Image / Wavelet Transform / KL Transform / Reversible Transform / Adaptive Prediction / Lossless Image Compression |
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| Research Abstract |
Two methods of efficient lossless image compression are investigated for six band Landsat Thematic Mapper imagery. The images of the region surrounding Komatsu airport, a field region and a mountaneous region in Ishikawa Prefecture are used for testing.
1. Intraband prediction and interband reversible wavelet transform(RWT).
First, to remove the spatial correlation between pixels within each band, 2D prediction is performed, by which the entropy of the images decreases about 25% in average. Secondly, to remove interband correlation, two samples of the shortest wave length band and its adjacent band in the same spatial position are RW transformed and the high frequency (HF) sample is transmitted. The low frequency (LF) sample is again 2-point RW transformed with the sample of the next long wave length band, and the HF sample is transmitted and so on. By this, the entropy decreases by 0.11 bpp.
2. Intraband RWT and interband reversible KL transform(RKLT)
First, each band-image is two layer RW transformed into seven subbands. In addition, for the lowest subband, 2D prediction is performed. The entropy of images decreases about 28% in average. Secondly, the same frequency subband samples of six band images are six-point KL transformed. Then, rows of KLT matrix are replaced each other such that, in each row, the diagonal element is the maximum; from it, RKLT matrix is derived. After that, the eigen images of the second layer subbands are classified by their variances into 8 classes, by which the entropy decreases about 0.2bpp.
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