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Optimal Recursive Bidirection Prediction for Hyperspectral Image Compression

机译:高光谱图像压缩的最佳递归双向预测

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In this paper, we propose a new algorithm named Optimal Recursive Bidirection Prediction (ORBP) for HyperSpetral Image (HSI) compression. Recursive Bidirection Prediction (RBP) is a good algorithm for HSI inter-band prediction. It's simple, efficient. But the algorithm can't get optimal prediction result under the sense of SNR. In this paper, the linear model of HSI is established, and the best prediction is deduced under the sense of SNR. The proposed method can get lower entropy after prediction. Computer simulation results show that compared with the traditional algorithm RBP, the proposed method ORBP has a great improvement by about 9.2738dB in SNR in average.
机译:在本文中,我们提出了一种名为最佳递归双向预测(ORBP)的新算法,用于近距离图像(HSI)压缩。递归双向预测(RBP)是HSI间歇预测的良好算法。这简单,高效。但是算法在SNR的感觉下无法获得最佳预测结果。在本文中,建立了HSI的线性模型,并在SNR感光下推导出最佳预测。所提出的方法可以在预测后获得较低的熵。计算机仿真结果表明,与传统算法RBP相比,所提出的方法ORBP平均在SNR中大约9.2738dB的改善。

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