Image Compression Using Improved SPIHT With DWT Matlab Project Code

ABSTRACT

      SPIHT is computationally very fast and among the best image compression algorithms known today. According to statistic analysis of the output binary stream of SPIHT encoding, propose a simple and effective method combined with Huffman encode for further compression. In this paper the results from the SPHIT algorithm are compared with the existing methods for compression like discrete cosine transform (DCT) and discrete wavelet transform (DWT).

      In recent years, wavelet transform as a branch of mathematics developed rapidly, which has a good localization property in the time domain and frequency domain, can analyze the details of any scale and frequency. So, it superior to Fourier and DCT. It has been widely applied and developed in image processing and compression. Wavelet Transform (WT) has received more and more significant attention in signal compression. However, many differences lie in the performance of different wavelets. There is a need to select the optimal matched wavelet bases to analyze the signal and the signal needs to be expressed with the fewest coefficients, i.e. sparse coefficients. The signal compression with wavelet is a procedure in which the input signal is expressed with a sum of a few of power terms for wavelet function. The more similar the bases function is to input signal, the higher the compression ratio is. But, at higher compression ratios we may experience more errors, i.e. mean square error will be high at the receiving end and hence PSNR will be very low.

      More improvements over DWT are achieved by SPIHT, by Amir Said and William Pearlman, in 1996 article, "Set Partitioning in Hierarchical Trees". In this method, more (wide-sense) zero-trees are efficiently found and represented by separating the tree root from the tree, so, making compression more efficient. Experiments are shown that the images through the wavelet transform, the wavelet coefficients‟ value in high frequency region are generally Small , so it will appear seriate "0" situation in quantify. SPIHT does not adopt a special method to treat with it, but direct output. In this paper, focus on this point, propose a simple and effective method combined with Huffman encode for further compression. A large number of experimental results are shown that this method saves a lot of bits in transmission, further enhanced the compression performance.

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