Lossy Compression Using Stationary Wavelet Transform and Vector Quantization Information Technology (original) (raw)

Compression is the art of representing the information in a compact form rather than in its original or uncompressed form. In other words, using the 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. If the algorithms used to encrypt work properly, there should be a significant difference between the original file and the compressed file. When the data compression is used in a data transmission application, speed is the primary goal. The speed of the transmission depends on the number of bits sent, the time required for the encoder to generate the coded message, and the time required for the decoder to recover the original ensemble. In a data storage application, the degree of compression is the primary concern. Compression can be classified as either lossy or lossless. Image compression is a key technology in the transmission and storage of digital images because of vast data associated with them. This research suggests an effective approach for image compression using Stationary Wavelet Transform (SWT) and Vector Quantization which is a Linde Buzo Gray (LBG) vector quantization in order to compressed input images in four phases; namely preprocessing, image transformation, zigzag scan, and lossy/lossless compression. Preprocessing phase takes images as input, so that the proposed approach resize the image in accordance with the measured rate of different sizes to (8 × 8) And then converted from (RGB) to (gray scale). Image transformation phase received the resizable gray scale images and produced transformed images using SWT. Zigzag scan phase takes as an input the transformed images in 2D matrix and produced images in 1D matrix. Finally, in lossy/lossless compression phase takes 1D matrix and apply LBG vector quantization as lossy compression techniques and other lossless compression techniques such as Huffman coding and arithmetic coding. The result of our approach gives the highest possible compression ratio and less time possible than other compression approaches. Our approach is useful in the internet image compression.

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