Integer wavelet transform for embedded lossy to lossless image compression
The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performances is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noises. The noises are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW [1] algorithm. Experiments are also performed to measure the difference in terms of bitrate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression.
wavelet transforms noise computation time computational complexity image coding image processing integer-to-integer wavelet transforms lossy compression performance low bit rates performance evaluation transform coding