Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
An efficient architecture for lifting-based two-dimensional discrete wavelet transforms
Integration, the VLSI Journal - Special issue: ACM great lakes symposium on VLSI
SPIHT image compression without lists
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
A VLSI architecture for lifting-based forward and inverse wavelettransform
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
High performance scalable image compression with EBCOT
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Evaluation of design alternatives for the 2-D-discrete wavelet transform
IEEE Transactions on Circuits and Systems for Video Technology
EURASIP Journal on Embedded Systems - Special issue on design and architectures for signal and image processing
Journal of Signal Processing Systems
International Journal of Sensor Networks
International Journal of Telemedicine and Applications
VLSI architecture of arithmetic coder used in SPIHT
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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In this paper, we present an implementation of the image compression technique set partitioning in hierarchical trees (SPIHT) in programmable hardware. The lifting based Discrete Wavelet Transform (DWT) architecture has been selected for exploiting the correlation among the image pixels. In addition, we provide a study on what storage elements are required for the wavelet coefficients. A modified SPIHT (Set Partitioning in Hierarchical Trees) algorithm is presented for encoding the wavelet coefficients. The modifications include a simplification of coefficient scanning process, use of a 1-D addressing method instead of the original 2-D arrangement for wavelet coefficients and a fixed memory allocation for the data lists instead of the dynamic allocation required in the original SPIHT. The proposed algorithm has been illustrated on both the 2-D Lena image and a 3-D MRI data set and is found to achieve appreciable compression with a high peak-signal-to-noise ratio (PSNR).