Multispectral Image Data Compression Using Classified Prediction and KLT in Wavelet Transform Domain(<Special Issue>Special Section on Papers Selected from ITC-CSCC 2002)

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概要

• 論文の詳細を見る

• This paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm in the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3-D SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• 社団法人電子情報通信学会の論文
• 2003-06-01

著者

• Kim S‐j

  Korea Inst. Construction Materials Seoul Kor

• Lee Kuhn-il

  School Of Electrical Engineering And Computer Science Kyungpook National University

• Kim Bong-seok

  School Of Electronic And Electrical Engineering Kyungpook National University

• Kim Seung-jin

  School Of Electronic And Electrical Engineering Kyungpook National University

• Kim Byung-ju

  School Of Electrical Engineering And Computer Science Kyungpook National University

• Park Kyung-nam

  Division Of Information Science Korea Nazarene University

• KIM Tae-Su

  School of Electronic and Electrical Engineering, Kyungpook National University

• Kim Tae-su

  School Of Electronic And Electrical Engineering Kyungpook National University

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• Multispectral Image Data Compression Using Classified Prediction and KLT in Wavelet Transform Domain(Special Section on Papers Selected from ITC-CSCC 2002)

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