Modern digital image processing requires powerful data compression algorithms to allow the data to be efficiently transferred from the host to end users (and back again). A typical 512times512 grayscale image of uncompressed data requires more than quarter of a million bytes. Current image compression standards like JPEG2000 and the FBI WSQ (wavelet scalar quantization) use wavelet transforms with quantization to compress still images, which reconstruct with high accuracy. This paper considers a number of popular 9/7 wavelet transform architectures. High level software models are developed for these transforms to validate their effectiveness. These software models are modified and evaluated as reversible integer wavelet lifting transforms. Further, using a virtual hardware design targeted to reconfigurable FPGA technology these transforms are implemented into a 2D discrete wavelet transform (DWT) image processor with DDR SDRAM operating at core speeds of 200+ MHz. Finally, our Matlab and Maple models perform the validation of wavelet lifting transforms
['Steven Bishop', 'Suresh Rai', 'Bahadir K. Gunturk', 'Jerry L. Trahan', 'Ramachandran Vaidyanathan']
Reconfigurable Implementation of Wavelet Integer Lifting Transforms for Image Compression