Novel multivariate vector quantization for effective compression of hyperspectral imagery

Although hyperspectral imagery (HSI) has been successfully deployed in a wide range of applications, it suffers from extremely large data volumes for storage and transmission. Consequently, coding and compression is needed for effective data reduction whilst maintaining the image integrity. In this paper, a multivariate vector quantization (MVQ) approach is proposed for the compression of HSI, where the pixel spectra is considered as a linear combination of two codewords from the codebook, and the indexed maps and their corresponding coefficients are separately coded and compressed. A strategy is proposed for effective codebook design, using the fuzzy C-mean (FCM) to determine the optimal number of clusters of data and selected codewords for the codebook. Comprehensive experiments on several real datasets are used for performance assessment, including quantitative evaluations to measure the degree of data reduction and the distortion of reconstructed images. Our results have indicated that the proposed MVQ approach outperforms conventional VQ and several typical algorithms for effective compression of HSI, where the image quality measured using mean squared error (MSE) has been significantly improved even under the same level of compressed bitrate.