Image coding using wavelet transform

TLDR

The authors propose an image compression scheme that incorporates psychovisual features in both spatial and frequency domains. The method first applies a biorthogonal wavelet transform to decompose the image across scales, then vector‑quantizes the coefficients with a multiresolution codebook and a noise‑shaping bit‑allocation strategy to enable progressive transmission. The wavelet transform proves especially suitable for progressive transmission of images.

Abstract

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.