The Application Of Wavelet Analysis In The Image Compression

With the development of the multi-medium and networks technology people has put forward the higher requirement at quality dimension and application in digital image, wanting to use limited space and bandwidth resources to transmit a big image and according to the real needs to gain reconfiguration image of different resolution or qualities, so the image compression is very necessary. Image compression is to transform the image data, to quantify the transformed data and to code the quantified data without data loss. Wavelet transform method is one of the most efficient methods for image compression, the core of all the image compressing methods based on wavelet is multi-resolution analysis and quantization and coding of the different scaling wavelet coefficients. The paper can be divided into three parts. The first part introduces not only the necessity and possibility of image compression but also the basic principle and method of image compression. The second part is focus on the basic of theory of wavelet analysis and Mallat algorithms. According to the image character, validity and possibility of image compression based on wavelet transform are proved. The third part, vector quantization algorithms are brought out and based on it, the paper puts forward a compression algorithm based on vector quantization and adaptive wavelet transform based on energy. That sub-images are whether to be compression or not is decided by their energy that is defined by certain criterion. Then the quantization and coding of some wavelet coefficients is given. In the procedure of quantization, this paper makes use of an improved LBG algorithm to generate codebook. Through experiments, not only the algorithm of this paper can adapt to various images, but also when their compression ratios are near, the quality of subjective vision and PSNR of this algorithm are better than JPEG algorithm.