An Algorithm with Low Complexity for Image Compression and its Hardware Implementation using VHDL

Image compression is highly essential for efficient transmission and storage of images in the field of communication engineering, bio-medical applications. Also, the compression technology is of special interest for the fast transmission and real-time processing on the internet. For reduced form and less capacity, the area of research growing day by day. The objective of image compression is to find a new representation in which pixels are less correlated, but with the original contents. In this paper, the existing as well as new algorithms are applied for compression for evaluation. The results have been compared for both techniques. On the basis of evaluating and analyzing the image compression techniques it presents the VHDL implementation of low complexity 2D-DWT approach applied to image compression. The decompression has to invert the transformations applied by the compression to the image data. When using the wavelet transform it is possible to exploit the unique properties of the wavelet coefficients to efficiently encode them.

[1]  Lai-Man Po,et al.  Adaptive lossy LZW algorithm for palettised image compression , 1997 .

[2]  José M. Solana,et al.  Haar wavelet based processor scheme for image coding with low circuit complexity , 2007, Comput. Electr. Eng..

[3]  Michael J. Gormish,et al.  Lossless and nearly lossless compression for high-quality images , 1997, Electronic Imaging.

[4]  Lee-Sup Kim,et al.  A real-time wavelet vector quantization algorithm and its VLSI architecture , 2000, IEEE Trans. Circuits Syst. Video Technol..

[5]  Michael J. Gormish,et al.  Next Generation Image Compression And Manipulation Using CREW , 1997, Proceedings of International Conference on Image Processing.

[6]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[8]  Luis Angel Barragan,et al.  VLSI Implementation of Discrete Wavelet Transform for Lossless Compression of Medical Images , 2001, Real Time Imaging.

[9]  David S. Taubman,et al.  Embedded block coding in JPEG 2000 , 2002, Signal Process. Image Commun..

[10]  Mariusz Duplaga,et al.  Low power FPGA-based image processing core for wireless capsule endoscopy , 2011 .

[11]  Syed Mahfuzul Aziz,et al.  Efficient parallel architecture for multi-level forward discrete wavelet transform processors , 2012, Comput. Electr. Eng..

[12]  Bogdan J. Falkowski,et al.  Lossless binary image compression using logic functions and spectra , 2004, Comput. Electr. Eng..

[13]  Mihir Narayan Mohanty,et al.  VLSI Design and Implementation for Adaptive Filter using LMS Algorithm , 2011 .

[14]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[15]  Din-Chang Tseng,et al.  Wavelet-based medical image compression with adaptive prediction , 2005, 2005 International Symposium on Intelligent Signal Processing and Communication Systems.

[16]  Rached Tourki,et al.  VLSI design of 1-D DWT architecture with parallel filters , 2000, Integr..

[17]  Mihir Narayan Mohanty,et al.  VHDL implementation of spatial filter for image enhancement , 2014, 2014 International Conference on Communication and Signal Processing.

[18]  Eero P. Simoncelli,et al.  Image compression via joint statistical characterization in the wavelet domain , 1999, IEEE Trans. Image Process..

[20]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..