The discrete wavelet transform (DWT) is a flexible tool in signal processing. Its use for image processing and particularly the matter of lossless and lossy compression hive been recognized in various studies. However, the ability of DWT to effectively represent image data is limited to smooth image regions. Discontinuities in the form of edges are expensive to code. We investigate the use of an adaptive transform to reduce the occurrences of large wavelet coefficients. A direction selection algorithm is introduced to subdivide the image into discrete blocks with each block assigned to an arbitrary direction. Transforms occurring between blocks are adapted to the direction of the concerned pixels to prevent boundary distortions. To encode the coefficients to a bitstream, a data clustering variant of SPIT is also introduced with the intention of lowering quantization errors for low bitrates. Preliminary test results based on PSNR and SSIM comparisons show a comparable performance to JPEG2000 even without the use of an entropy encoder
