Email this article Image Compression Based on Multilevel Adaptive Thresholding using Meta-Data Heuristics
Abstract
Satellite image processing involves very often the need of compression. The compression of satellite images will reduce storage requirements and conserves transmission bandwidth. In this paper, a lossy image compression method is proposed based on multilevel adaptive thresholding using Meta-Data heuristics to compress the Landsat-8 satellite images. In the proposed method the number of thresholds is fixed in accordance with the bitrate required and the Peak Signal to Noise Ratio (PSNR) is improved by entropy based adaptive thresholding. Test image of Landsat-8, Band 3, 5 is used for performing the compression and the performance metric PSNR is measured for uniform thresholding and the proposed method. The proposed method gives improvement in the PSNR and the method is computationally simulated using Fixed Point Binary scaling.
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*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)