Improved Wavelet-Based Embedded Image Coding Using a Dynamic Index Reordering Vector Quantizer

In this paper, we propose a temporal dynamic index reordering vector quantization for wavelet-based embedded coding. Da Silva et al. introduced a vector quantization concept that is similar to EZW called a successive approximation vector quantizer (SAVQ). The successive refinement process is defined as a temporal process in our proposed algorithm. The temporal updates are performed in every refinement pass, and the updates of codevectors reflect the updates of angles for vector approximation. Because the approximation trajectory of SAVQ is similar to that of the least-mean- square algorithm, temporal redundancy does not seem to be obvious. However, the redundancy becomes more clear in the improved SAVQ. By carefully analyzing the angle transitions, we are successfully able to apply dynamic index reordering vector quantization (DIRVQ) in the temporal domain and improve the coding performance. Efficient encoding and decoding algorithms for D₄ lattice vector quantization are also proposed, and the algorithms can be applicable to DIRVQ to reduce the computational complexity of the reordering process. Experiments are performed with the 2x2 vector size. For almost all tested bpp´s the PSNR performance of our proposed algorithm for a lena image outperforms that of SAVQ up to 0.18 dB. Improvement is measured for the highly detailed baboon image as well.