Performance Assessment of Image Compression and Reconstruction Employing Wavelet Transform with Lifting Scheme: An Effective Approach
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Abstract
In the analysis for image compression and subsequent reconstruction, the primary objective is to
minimize the data redundancy in terms of spatial and frequency domain without much affecting
the image quality. Lossless reversible image compression model is proposed in case of
continuous and discrete time utilizing integer wavelet transform (IWT) that incorporates lifting
scheme (LS) excellently. As compared to wavelet decomposition, here both approximation as
well as detailed contents of the image under consideration are further decomposed, which
enhances the compression ratio. Bi-orthogonal wavelets are constructed using LS that makes use
of both high pass and low pass filter values along with addition and shift operations on the
resulting wavelet coefficients quite suitably. Forward as well as inverse lifting schemes are
implemented to reduce the computational complexity and achieve superior image compression
performance that accounts for encoding time, decoding time, peak signal to noise ratio (PSNR)
and better compression ratio (CR). The present paper projects the utility of IWT and LS in the
domain of image compression and reconstruction that will immensely benefit the researchers and
experimentalists involved with image and signal processing.
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