Chip Design of DWT for Image Compression
Arvind Singh Bisht, Monika Gupta, M. Tech · 2015
The wavelet analysis is used to divide the complete information of a digital image into the detailed sub images and approximated signals. The approximation values of the sub signal generally show the pixel values of an image. The basic idea of the wavelet transform is to represent any arbitrary function f as a superposition of wavelets. Any such superposition decomposes f into different scale levels, where each level is then further decomposed. Like Fourier Transform, which is the sum of all time signal f(t) multiplied by a complex exponential and the result of the transform are the Fourier coefficients. The continuous wavelet transform is defined as the sum overall time of signal multiplied by scaled, shifted version of wavelet function. The research paper focuses on the VHDL design and development of image compression algorithm Discrete Wavelet Transform (DWT). The work is based on the HAAR DWT, in which the image is divided into LL, LH, HL and HH band in first level of decomposition, further decomposition can take place with respect to each band. In the second level of decomposition the LL band is subdivided into LLLL, LLLH, LLHL and LLHH sub bands. The design is developed in Xilinx ISE software 14.1 and functionally simulated in Modelsim9.0 software.