Adaptive Wavelet Transform for Image Compression via Directional Quincunx Lifting

We propose a novel adaptive wavelet transform that exploits local image properties for image compression. It combines wavelet filters adaptive to edge orientations with quincunx subsampling to form a 2-D nonseparable transform through lifting. Filter selections are efficiently represented. Significant improvement on both subjective and objective quality over the conventional separable transform is observed. In addition, unlike previous adaptive transforms, the symmetry in quincunx subsampling enables even quality for image features along different directions and the compression performance is insensitive to image orientation