中文说明：应用背景在这个代码中，使用离散小波变换的新的混合技术变换（DWT）了。我们使用电源信号显示评价噪声比（PSNR）作为衡量质量，我们表明，小波变换阈值，量化，和游程编码和Huffman编码的组合为阶段，提供了比JPEG更好的PSNR性能和我们可以得到一个重要的铬。我们的算法是一样的：阅读图像——>；小波变换转型——>；阈值量化————>；>；RLE编码——>；哈夫曼编码——>；在保存文件压缩图像（*。HDWT）减压步骤我们要打开的文件（*。HDWT）后，步骤是这样：Huffman解码——>；> RLE解码—；量化反——>；IDCT变换——>；打开图像为BMP图像文件：compdwt。μm的：一种图像我们只能运行compdct压缩编码主。Mdecompdwt。μm的：要重建我们的形象，我们在这里的意思是如果我们要解压缩文件之前得到（。HDWT）只有我们运行这个代码并选择文件感兴趣。调整。M：M：频率计算概率。M：游程编码RLE。IRLE。M：逆运行长度编码abais。M：降低值大于255关键技术 ；一种新的混合技术；小波变换阈值，量化，游程编码，霍夫曼，PSNR，CR。& nbsp；& nbsp；
English Description：
Application backgroundIn this code, a new hybrid technique using the discrete wavelet transform (DWT) is presented. We show evaluation using the Power Signal to Noise Ratio (PSNR) as a measure of quality, we show that DWT with threshold, Quantization, and combination of RLE and Huffman as coding stage, provides a better performance than JPEG in terms of PSNR and we can get an important CR. Our Algorithm is like that: Reading image-->DWT transformation-->Thresholding-->Quantization-->RLE encoding--> Huffman encoding-->Save a compressed image in file (*.Hdwt) in decompression steps we should open file(*.Hdwt) aafter that the steps will be like that: Huffman decoding-->RLE decoding--> Quantization inverse-->IDCT transformation-->Open image as Bmp image Files: compdwt.m: main code to compress an image we only run compdct.m decompdwt.m: To reconstruct ou