Abstract
Color Image Compression Based on Wavelet, Differential Pulse Code Modulation and Quadtree Coding
Highlights
The need for data compression as a topic acquired its importance because it is a solution key for pypass the insufficient storage space and limited bandwidth of data transmission (Havaldar and Medioni, 2004; Salomon, 2004)
Different sets of tests been performed to evaluate the performance of the proposed color image compression system in terms of Compression Ratio (CR), Peak Signal to Noise ratio (PSNR)
The results show clearly an increase in CR is occurred when Differential Pulse-Code Modulation (DPCM) is applied; insignificant change in PSNR and MSE is occurred
Summary
The need for data compression as a topic acquired its importance because it is a solution key for pypass the insufficient storage space and limited bandwidth of data transmission (Havaldar and Medioni, 2004; Salomon, 2004). The programs used to compress still images are, using the designed techniques that exploit unimportant sensory information and statistical redundancies. More images program-ers rely on the use of two techniques (i.e., sub-band coding and transform coding). Sub-band coding decomposes signal into a number of sub-bands, using band-pass filter like wavelet transform (Katz and Gentile, 2005). Transform coding uses a mathematical transformation like DCT and FFT. The concept of wavelet coding, like other transform coding techniques, is based on the idea that the coefficients of transform decorrelates the samples values of the signal, such that they can be coded in more compressive way in comparison with the case of direct compression of the original samples values themselves (Dhubkarya and Dubey, 2009)
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