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In the remote sensing applications compression method is most significant, using mean square error. In the vision based application, the PSNR is higher which shows the good visual quality. In existing compression methods it consider the human visual system for designing natural images, which does not consider the unique feature of remote sensing images. Based on this problem we consider human vision based adaptive scanning scheme for the application of remote sensing image compression. In this first apply the wavelet transform for an image, then establish the retina based visual sensitivity model and then generates the visual weighting mask. Secondly after calculate the scanning order of an weighted mask image in a subband and within a subband, we are using adaptive scanning method. Finally the binary tree codec is used for the encoding and decoding the image. Objective metrics shows that as compared to the other compression method, HAS based compression method provides a good visual quality which is mainly suitable for the vision based application in the case of remote sensing images.
Keywords:
Adaptive scanning, human visual system(HVS), remote sensing image compression, binary tree coding.
Cite Article:
"HVS-Based Adaptive Scanning for The compression of images", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 8, page no.120 - 125, August-2017, Available :http://www.ijrti.org/papers/IJRTI1708021.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator