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Compression is the art of representing the information in a compact form rather than its original or uncompressed form. In other words, using data compression, the size of a particular file can be reduced. This is very useful when processing, storing or transferring a huge file, which needs lots of resources. Lossy compression techniques are one in which compressing data and then decompressing it retrieves data that will be different from the original, but it is enough to be useful in some way. Lossy data compression is used frequently on the Internet and mostly in streaming media and telephony applications. In lossy data repeated compressing and decompressing a file will cause it to lose quality. Lossless, when compared with lossy data compression, will retain the original quality, efficient and minimum hardware implementation for the data compression and decompression needs to be used even though there are so many compression techniques which are faster, memory efficient which suits the requirements of the user. Stationary wavelet transforms among the different tools of multi-scale signal processing, wavelet is a time-frequency analysis that has been widely used in the field of image processing. The Stationary wavelet transform (SWT) is a wavelet transform algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transforms (DWT). Translation-invariance is achieved by removing the downsamplers and upsamplers in the DWT and upsampling the filter coefficients by a factor of 2(j-1)in the jth level of the algorithm. The SWT is an inherently redundant scheme as the output of each level of SWT contains the same number of samples as the input so for the decomposition of N levels there is a redundancy of N in the wavelet coefficients. This Matlab design shown the performance of SWT in terms of PSNR value.
Reference Paper: Hybrid Compression based Stationary Wavelet Transforms
Author’s Name: Omar Ghazi Abbood,Mahmood A. Mahmood,Hend A. Elsayed and Shawkat K. Guirguis
Source: IJMETMR
Year:2016
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