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• W4211079263 abstract "The instinctive essence of the Hyperspectral imagery cube is its immense information having both spatial and spectral correlation. In the interest of reducing the storage and bandwidth requisites, an effective lossless Hyperspectral compression system is proposed. The imagery is enforced to preprocessing stage preceding decorrelation. Preprocessing stage comprises band normalization and band ordering techniques. A technique named Greedy heap sorting is addressed to sort the bands. The proposed plan accords with a Compression ratio (CR) of 8.12 and bits per pixel (bpp) of 1.67. The performance of the system is comparable to earlier algorithms for lossless Hyperspectral image compression concerning compression ratio and bpp. An experiment conducted on AVIRIS images substantiates that the methodology presented surpasses the IP3-OBPS-BPS method by a percentage increase of 116.44 in CR and a percentage decrease of 58.49 in bpp. The multiscale High Dynamic Range (HDR) approach is used to project Hyperspectral images on devices with Low Dynamic Range (LDR) devices. The Bilateral filter is used for the decomposition of the image into multiple base layers and detail layer. The PSNR obtained is 37.4911 for the compressed HDR image, which signifies the better quality of the reconstructed image with a 4.8% compression ratio." @default.
• W4211079263 created "2022-02-13" @default.
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• W4211079263 date "2022-02-09" @default.
• W4211079263 modified "2023-09-27" @default.
• W4211079263 title "Lossless Compression of Hyperspectral Imagery by Assimilating Decorrelation and Pre-processing with Efficient Displaying Using Multiscale HDR Approach" @default.
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• W4211079263 doi "https://doi.org/10.1080/03772063.2022.2028581" @default.
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