Technologies for Digital Image Compression -A Review

Compression is one of the most important applications of wavelets. The compression procedure contains three steps. First is Decompose in which Choose a wavelet, choose a level N. Compute the wavelet decomposition of the signal at level N. Second is Threshold detail coefficients where For each level from 1 to N, a threshold is selected and hard thresholding is applied to the detail coefficients. The third one is Reconstruct which Compute wavelet reconstruction using the original approximation coefficients of level N and the modified detail coefficients of levels from 1 to N. Second approach Vector quantization (VQ) is a lossy data compression method based on the principle of block coding. It is a fixed-to-fixed length algorithm. In the earlier days, the design of a vector quantizer (VQ) was considered to be a challenging problem due to the need for multi-dimensional integration. In 1980, Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence. The use of a training sequence bypasses the need for multi-dimensional integration. A VQ that is designed using this algorithm are referred to in the literature as an LBG-VQ. This paper presents a review of existing digital image compression techniques.