Image Compression Hybrid using DCT , DWT , Huffman
Harjeetpal Singh, Sakshi Rana · 2012
Image compression literally means reducing the size of graphics file, without compromising on its quality. Depending on whether the reconstructed image has to be exactly same as the original or some unidentified loss may be incurred, two techniques for compression exist. Image compression is an essential technology in multimedia and digital communication fields. Ideally, an image compression technique removes redundant and/or irrelevant information, and efficiently encodes what remains. Practically, it is often nec essary to throw away both on redundant information and relevant information to achieve the required compression. in either case, the trick is finding methods that allow important information to be efficiently extracted and represented. Most of the existing image coding algorithm is based on the correlation between adjacent pixels and therefore the compression ratio is not high. Fractal coding is a potential image compression method, which is based on the ground breaking work of Barnsley and was developed to a usable state by Jacquin.Its essence is that correlation not only exists in adjacent pixels within a local region, but also in different regions and local regions with global regions. The fractal-based schemes exploit the self-similarities that are inherent in many real world images for the purpose of encoding an image as a collection of transformations. Here in this hybrid model we are going to proposed a Nobel technique which is the combination of several compression techniques. This paper presents DWT and DCT implementation because these are the lossy techniques and in the last we introduce Huffman decoding technique which is lossless. At the last we implement lossless technique so our PSNR and MSE will go better than the old algorithms and due to DWT and DCT we will get good level of compression.