Srikanth, Sure (2013) Compression Efficiency for Combining Different Embedded Image Compression Techniques with Huffman Encoding. MTech thesis.
This thesis presents a technique for image compression which uses the different embedded Wavelet based image coding in combination with Huffman- encoder(for further compression). There are different types of algorithms available for lossy image compression out of which Embedded Zerotree Wavelet(EZW), Set Partitioning in Hierarchical Trees (SPIHT) and Modified SPIHT algorithms are the some of the important compression techniques. EZW algorithm is based on progressive encoding to compress an image into a bit stream with increasing accuracy. The EZW encoder was originally designed to operate on 2D images, but it can also use to other dimensional signals. Progressive encoding is also called as embedded encoding. Main feature of ezw algorithm is capability of meeting an exact target bit rate with corresponding rate distortion rate(RDF). Set Partitioning in Hierarchical Trees (SPIHT) is an improved version of EZW and has become the general standard of EZW. SPIHT is a very efficient image compression algorithm that is based on the idea of coding groups of wavelet coefficients as zero trees. Since the order in which the subsets are tested for significance is important in a practical implementation the significance information is stored in three ordered lists called list of insignificant sets (LIS) list of insignificant pixels (LIP) and list of significant pixels (LSP). Modified SPIHT algorithm and the preprocessing techniques provide significant quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity as compared to the previous techniques. This proposed method can reduce redundancy to a certain extend. Simulation results show that these hybrid algorithms yield quite promising PSNR values at low bitrates.
|EZW, SPIHT, progressive encoding, Huffman encoder , PSNR and bit rates.
|Engineering and Technology > Electronics and Communication Engineering > Image Processing
|Engineering and Technology > Department of Electronics and Communication Engineering
|31 Oct 2013 14:03
|20 Dec 2013 14:50
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