Orientation Adaptive Discrete Packet Wavelet Decomposition via Shifting Operators for Image Compression

In this paper we present novel techniques to adapt conventional wavelet transforms to follow locally oriented features found in images. We introduce a shift operator before each step in a lifting implementation of the DWT. The best shifts are estimated by minimizing the high-pass coefficient energy and then used in both the prediction and update lifting steps. To approximate the asymptotically optimal rate-distortion performance of a piece-wise regular function more closely, we adopt a packet wavelet decomposition. Experimental results obtained integrating the proposed transform into the JPEG2000 codec show improvements in both visual and objective tests, allowing for a "better" representation of the edges at very-low rates. Very recently, some related ideas have been presented by other authors. The most distinctive features of this paper include a more flexible packet wavelet decomposition structure and a comparison between subband- and image-domain shifting operators.