Hybrid image compression model based on subband coding and edge-preserving regularisation
S.-W. Hong, Paul G. Bao · IEE Proceedings - Vision Image and Signal Processing · 2000
An edge-preserving image compression model is presented, based on subband coding and iterative constrained least square regularisation. The idea is to incorporate the technique of image restoration into the current lossy image compression schemes. The model utilises the edge information extracted from the source image as a priori knowledge for the subsequent reconstruction. Generally, the extracted edge information has a limited range of magnitudes and it can be lossily conveyed. Subband coding, one of the outstanding lossy image compression schemes, is incorporated to compress the source image. Vector quantisation, a block-based lossy compression technique, is employed to compromise the bit rate incurred by the additional edge information and the target bit rate. Experiments show that the approach could significantly improve both the objective and subjective quality of the reconstructed image by preserving more edge details. Specifically, the model incorporated with SPIHT (set partitioning in hierarchical trees) outperformed the original SPIHT with the `Baboon' continuous-tone test image. In general, the model may be applied to any lossy image compression systems.