Lossless Image Compression using Hybridization of Entropy Encoding and Data Folding

Abstract: The compression models play an important role in saving the storage space on the local or online storage or for the smooth transfers over the give network architecture. The robust and lossless compression is always remained as the point of interest for the researchers. The higher compression ratios heavily impact the data quality, which has turned the research over compression in various regions of the world. There are several entropy encoding models, arithmetic encoding like encoding techniques, etc has already been used for the purpose of data compression. The wavelet models have also been proposed in various compression models under various research projects. In this paper, the author worked over the robust, stronger and lossless compression model for various model of data using the amalgamation of Huffman encoding, Run-length encoding, LZW encoding along with Discrete wavelet transform (DWT) based wavelet compression model in order to provide the highly robust and lossless compression. The discrete wavelet transform (DWT) has already been proved as the best image compression algorithm. The DWT technique decomposes the image matrix into various sub-matrices to create a compressed image. The new compression technique will be developed by combining the most effective and fast wavelets of DWT technique for image compression. The quality of the new image compression technique will be evaluated using the peak signal to noise ratio (PSNR), mean squared error (MSE), compression ratio (CR) and elapsed time (ET). Also, the new techniques would be compared with the existing image compression techniques on the basis of the latter mentioned parameters. The experimental results have proved the effectiveness of the proposed model in the terms of MSE, CR and ET.

Keywords: Huffman encoding, run-length encoding, LZW encoding, DWT.