Adaptive Wavelet Difference Reduction using High-Order Context Modeling for Embedded Image Compression

The new embedded algorithm for wavelet image compression is proposed. The main idea of the algorithm is to use high-order statistical context modeling for significant coefficients prediction by scanning order adaptation of wavelet difference reduction (WDR). The new predefined scanning order, header, preprocessing of all-lowpass coefficients are used together with the scanning order adaptation to improve the rate-distortion performance of the image coder, while retain the important features of the state of the art image coder from original WDR, such as embedded/progressive coding, region of interest support, and support operation on compressed data. The high-order context model used can be fixed or adaptive model. Although at the very beginning state, this technique, using simple fixed model, in PSNR sense, considerably surpasses set partitioning in hierarchical trees (SPIHT) in no arithmetic coding mode for all test images at all bit rates, and also outperforms JPEG2000 in high compression ratio (very low to low bit rate) for many images in the test set