基于多分辨率级小波变换的图象压缩方法

随着国际互联网的出现,使得越来越多的图象信息传输交流越来越便捷,但传输速度始终是制约网络发展的重要因素,这也使得对图象进行压缩的要求更加迫切。小波变换的良好空间一频率局部化特性,使得原始图象的能量大部分聚集到了低频子带.为了提高图象压缩的效率和重建图象的质量,利用原始图象在小波分解中不同分辨率级能量分布不均匀的特点,提出了一种对各分辨率级进行分级处理的设计方法,即通过对各分解级量化因子的评价,为该级获取一个最佳的量化因子来进行压缩.实验证明,该方法在提高图象的压缩效率和重构质量方面取得了良好的效果.

The Method of Wavelet Image Compression Based on Multiresolution Levels

The good spatial-frequencial localization characteristics of wavelet transform make the energy of image congregate mostly in lower frequency subimage. This paper utilizes the property of the asymmetrical energy distribution on different multiresolution levels, and introduces a method which is called multiresolution levels compression (MLC) to process the image on the levels of different multiresolution. By the estimate and studying of the quantization coefficients during wavelet transform on every decomposed level, the experiment can get the best quantization coefficient for any particular level. Theoretical analysis and experimental results demonstrate that a method of higher compression rate is implemented. In the experiment, the best quantization coefficient of every level is determined by the data analysis of human. For the aim to process huge amount of images such as the images from the Internet, the technology of artificial intelligence and machine learning can be used to let the computer use MLC method by studying a certain number of image examples and then process more images automatically. The ability to use the MLC method would also improve according to the amount of images the computer has studied. This is the new direction to perform in the near future.