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A Novel Optimized Golomb-Rice Technique for the Reconstruction in Lossless Compression of Digital Images
Author(s) -
Shaik Mahaboob Basha,
B. C. Jinaga
Publication year - 2013
Publication title -
isrn signal processing
Language(s) - English
Resource type - Journals
eISSN - 2090-505X
pISSN - 2090-5041
DOI - 10.1155/2013/539759
Subject(s) - lossless compression , computer science , image compression , data compression , artificial intelligence , computer vision , lossy compression , golomb coding , texture compression , data compression ratio , image quality , compression ratio , peak signal to noise ratio , compression (physics) , image processing , algorithm , image (mathematics) , materials science , composite material , internal combustion engine , automotive engineering , engineering
The research trends that are available in the area of image compression for various imaging applications are not adequate for some of the applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm based on Golomb-Rice coding which was efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of peak signal-to-noise ratios (PSNR). We considered medical images, satellite extracted images, and natural images for the inspection and proposed a novel technique to increase the visual quality of the reconstructed image.

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