This paper presents a new generic algorithm for image interpolation as well as lossless image coding. Main motivation behind the work is to reduce computational complexity involved in using Least Square Error Minimization (LS). The proposed method down samples the given image to its quarter size and then to its (1/16)th size. For each downsampled image, the least Square predictors are then obtained corresponding to pixels belonging to each bin. Thus, these predictors are used to synthetically generate a set of optimal predictors corresponding to each bin of the original image. Our proposed algorithm thus reduces 60% to 70% of computational complexity. We also observed that proposed algorithm gives insignificant loss in terms of compression ratio as compared with some of the previous works reported in literature.

Low Complexity Algorithm for Interpolation as well as Lossless Compression of Natural Images

RAMPONI, GIOVANNI
2013-01-01

Abstract

This paper presents a new generic algorithm for image interpolation as well as lossless image coding. Main motivation behind the work is to reduce computational complexity involved in using Least Square Error Minimization (LS). The proposed method down samples the given image to its quarter size and then to its (1/16)th size. For each downsampled image, the least Square predictors are then obtained corresponding to pixels belonging to each bin. Thus, these predictors are used to synthetically generate a set of optimal predictors corresponding to each bin of the original image. Our proposed algorithm thus reduces 60% to 70% of computational complexity. We also observed that proposed algorithm gives insignificant loss in terms of compression ratio as compared with some of the previous works reported in literature.
9789531841870
9789531841948
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2760563
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