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- W2182912186 abstract "Multispectral images are images with high spatial and spectral resolution. Efficient multispectral image compression plays a key role in most of the geographical applications. The three important phases involved in the proposed adaptive technique are contrast stretching, clustering and encoding based on the resultant clusters. The contrast stretching results in a very clear image and the image is then clustered into smooth and textured regions based on K means algorithm. The spatial orientation tree (STW) wavelet algorithm and the wavelet difference reduction (WDR) algorithm are applied to the smooth and textured regions respectively. The results are compared using the Compression ratio, Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) Index metrics. It reflects the quality of the proposed work as betterment than the existing state of art techniques with very high compression ratio and minimum image distortion." @default.
- W2182912186 created "2016-06-24" @default.
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- W2182912186 date "2014-01-01" @default.
- W2182912186 modified "2023-09-27" @default.
- W2182912186 title "A NOVEL SYNTAX CLASS BASED ADAPTIVE ENCODING TECHNIQUE FOR CONTRAST STRETCHED MULTISPECTRAL IMAGES" @default.
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