Segmentation-Driven Direction-Adaptive Discrete Wavelet Transform

This paper proposes a novel segmentation-driven direction-adaptive discrete wavelet transform (SD DADWT), wherein the adaptation of the directional wavelet bases is performed on the segments describing the natural geometry of the image. First, a multi-resolution segmentation of the image is performed, obtained through an Edgmentation procedure. The optimum lifting directions are then selected for each segment and at each resolution. The proposed SD DADWT retains the inherent advantages offered by a multiresolution representation of the geometric features in the image, and in the same time provides a sparse image representation via DADWT. Preliminary experimental results obtained in a coding application show that the visual quality of the reconstructed image can be further improved by applying a geometrically-oriented transform on segments that approximate the natural borders in the image.