CNRS, Univ. de Nice-Sophia Antipolis, Valbonne.
IEEE Trans Image Process. 1992;1(2):205-20. doi: 10.1109/83.136597.
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.
提出了一种同时考虑空间域和频率域心理视觉特征的图像压缩方案。该方法包括两个步骤。首先，使用小波变换以获得一组双正交子类图像：使用金字塔算法结构对原始图像进行不同尺度的分解。分解沿着垂直和水平方向进行，并保持描述图像所需的像素数量不变。其次，根据香农的率失真理论，使用多分辨率码本来对小波系数进行矢量量化。为了对小波系数进行编码，提出了一种噪声整形比特分配过程，该过程假设高分辨率的细节对人眼的可见度较低。为了允许接收器以最小的代价尽快识别图片，提出了一种渐进传输方案。结果表明，小波变换特别适合渐进传输。
