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A High Performance Modified SPIHT for Scalable Image Compression
Bibhuprasad Mohanty, Abhishek Singh, Sudipta Mahapatra
Pages - 390 - 402     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Zero Shifting, 2D SPIHT, Lifting Wavelet Transform, Context Modeling, Resolution Scalability
In this paper, we present a novel extension technique to the Set Partitioning in Hierarchical Trees (SPIHT) based image compression with spatial scalability. The present modification and the preprocessing techniques provide significantly better quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity. There are two proposals for this paper. Firstly, we propose a pre-processing scheme, called Zero-Shifting, that brings the spatial values in signed integer range without changing the dynamic ranges, so that the transformed coefficient calculation becomes more consistent. For that reason, we have to modify the initialization step of the SPIHT algorithms. The experiments demonstrate a significant improvement in visual quality and faster encoding and decoding than the original one. Secondly, we incorporate the idea to facilitate resolution scalable decoding (not incorporated in original SPIHT) by rearranging the order of the encoded output bit stream. During the sorting pass of the SPIHT algorithm, we model the transformed coefficient based on the probability of significance, at a fixed threshold of the offspring. Calling it a fixed context model and generating a Huffman code for each context, we achieve comparable compression efficiency to that of arithmetic coder, but with much less computational complexity and processing time. As far as objective quality assessment of the reconstructed image is concerned, we have compared our results with popular Peak Signal to Noise Ratio (PSNR) and with Structural Similarity Index (SSIM). Both these metrics show that our proposed work is an improvement over the original one.
CITED BY (12)  
1 Juliet, S., Rajsingh, E. B., & Ezra, K. (2015). Compression of medical images for remote diagnosis based on geometric transforms. International Journal of Telemedicine and Clinical Practices, 1(1), 17-31.
2 CHACKO, S. M. high performance adaptive binary arithmetic coder used in spiht.
3 Chacko, S. M. High Speed Adaptive Binary Arithmetic Coder used in SPIHT.
4 Shiby Angel, K. medical image analysis and processing using a dual transform.
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6 SB, F. A. B. P. P. (2014). Improvement in Traditional Set Partitioning in Hierarchical Trees (SPIHT) Algorithm for Image Compression.
7 Sumitra, P. A New, Fast and Efficient Wavelet Based Image Compression Technique Using JPEG2000 with EBCOT versus SPIHT.
8 Harika, K., & Reddy, K. R. (2013). Design and Implementation of Arithmetic Coder Used in SPIHT. International Journal of Innovative Technology and Exploring Engineering (IJITEE).
9 Galiano, V., López-Granado, O., Malumbres, M. P., & Migallón, H. (2013). Multicore-based 3D-DWT video encoder. EURASIP Journal on Advances in Signal Processing, 2013(1), 1-12.
10 Saravanan, S., Juliet, D. S., & Angel, K. S. (2013). Medical Image Compression using Modified Curvelet Transform. International Journal of Advanced Research in Computer Science, 4(2).
11 Mohanty, B., & Mohanty, M. N. (2013, December). A Novel SPECK Algorithm for Faster Image Compression. In Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on (pp. 479-482). IEEE.
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Mr. Bibhuprasad Mohanty
Mr. Abhishek Singh
ICFAI university, Tripura - India
Dr. Sudipta Mahapatra
Indian Institute of Technology, Kharagpur - India