Earticle

In this paper, a simplified structure of integer lifting wavelet filter banks for lossless image compression is proposed by shifting and merging the scaling factors of the row and the column wavelet transforms. It is implemented by reducing the numbers of scaling factors and considering the scaling lifting. The numbers of scaling factors of the 2-D wavelet transform can be reduced by shifting and merging operation, and then the computing speed can be improved. Furthermore, the scaling lifting of simplified structure can be used to reduce the computing errors and get more accurate results. Experiments show that the simplified integer lifting structure results in lesser computational steps than the standard integer lifting structure and therefore improves the speed of the image compression. Besides, using the new lossless image compression system based on simplified integer lifting wavelet, the lower bit-rates are obtained.

In this paper, we propose an efficient architecture for 2D IWT using an existing 1D IWT. Lifting based IWT is the architecture of which a multiplier is replaced by adders and shift registers. The structure is relatively simple and modular. The proposed architecture to process an image size with 256x256 pixels consists of 16 adders, 8 shift registers, and some memories. By processing two rows at the same time, 2D sub-band coefficients can be calculated immediately after 1D sub-band coefficients have been processed. The architecture is designed so that each image can be inputted consecutively. The number of adders and shift registers is increased by twice comparing the existing architecture, but the memory size and the execution time are decreased by half. The proposed architecture is implemented using Verilog-HDL and simulated using iSim. It is synthesized and demonstrated at ISE for xc5vlx330 in RPS3K board.

This paper focuses on lossless medical image compression methods for medical images that operate on three-dimensional(3-D) irreversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm to medical images, using a 3-D wavelet decomposition and a 3-D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling( 2) and truncations keep the integer precision and the transform unitary. We have tested our encoder on volumetric medical images using different integer filters and different coding unit sizes. The coding unit sizes of 16 slices save considerable dynamic memory(RAM) and coding delay from full sequence coding units used in previous works. Results show that, even with these small coding units, our algorithm with certain filters performs as well and better in lossless coding than previous coding systems using 3-D integer wavelet transforms on volumetric medical images.