《信息论与编码》实验报告.docx

信息论与编码实验报告QQ417860489一、实验目的1、理解信源编码的基本原理；2、熟练掌握Huffman编码的方法；3、理解无失真信源编码和限失真编码方法在实际图像信源编码应用中的差异。二、实验设备与软件 1、PC计算机系统 2、VC+6.0语言编程环境 3、基于VC+6.0的图像处理实验基本程序框架imageprocessing4、常用图像浏览编辑软件Acdsee和数据压缩软件winrar。5、实验所需要的bmp格式图像（灰度图象若干幅）。三、实验内容与步骤 1、针对“图像1.bmp”、“图像2.bmp”和“图像3.bmp”进行灰度频率统计（即计算图像灰度直方图），在此基础上添加函数代码构造Huffman码表，针对图像数据进行Huffman编码，观察和分析不同图像信源的编码效率和压缩比。（1）各个图像的灰度频率统计如下（2）对图像1进行Huffman编码，得到码表如下像素值概率分布Huffman编码（未差分）像素值概率分布Huffman编码（未差分）像素值概率分布Huffman编码（未差分）00.0118360001101000.000000011001100110010112000.000000011001100001011010.000000011001100100000001010.01003311001012010.000000011001100001011120.000000011001100100000011020.000000011001100110011002020.01598310010130.000000011001100100000101030.000000011001100110011012030.000000011001100001100040.0213091111011040.000000011001100110011102040.000000011001100001100150.000000011001100100000111050.0194301110002050.000000011001100001101060.000000011001100100001001060.000000011001100110011112060.01524101111070.000000011001100100001011070.000000011001100110100002070.000000011001100001101180.022653000011080.000000011001100110100012080.000000011001100001110090.000000011001100100001101090.0133950100112090.0000000110011000011101100.000000011001100100001111100.000000011001100110100102100.012887010001110.000000011001100100010001110.000000011001100110100112110.0000000110011000011110120.0037240110011011120.000000011001100110101002120.0000000110011000011111130.000000011001100100010011130.0140170101112130.0000000110011000100000140.000000011001100100010101140.000000011001100110101012140.0000000110011000100001150.000000011001100100010111150.000000011001100110101102150.017783101101160.022806000101160.000000011001100110101112160.0000000110011000100010170.000000011001100100011001170.0164751001112170.0000000110011000100011180.000000011001100100011011180.000000011001100110110002180.0000000110011000100100190.000000011001100100011101190.000000011001100110110012190.012412001100200.004404011001111200.000000011001100110110102200.0000000110011000100101210.000000011001100100011111210.0164941010002210.0000000110011000100110220.000000011001100100100001220.000000011001100110110112220.0000000110011000100111230.000000011001100100100011230.000000011001100110111002230.020192111001240.0189911101011240.000000011001100110111012240.0000000110011000101000250.000000011001100100100101250.0181411100002250.0000000110011000101001260.000000011001100100100111260.000000011001100110111102260.0000000110011000101010270.000000011001100100101001270.000000011001100110111112270.017126101010280.0211981110111280.000000011001100111000002280.0000000110011000101011290.000000011001100100101011290.000000011001100111000012290.0000000110011000101100300.000000011001100100101101300.0139580101102300.0000000110011000101101310.000000011001100100101111310.000000011001100111000102310.015326011111320.01028311111001320.000000011001100111000112320.0000000110011000101110330.000000011001100100110001330.000000011001100111001002330.0000000110011000101111340.000000011001100100110011340.0125940011102340.0000000110011000110000350.000000011001100100110101350.000000011001100111001012350.012412001101360.0212701111001360.000000011001100111001102360.0000000110011000110001370.000000011001100100110111370.000000011001100111001112370.0000000110011000110010380.000000011001100100111001380.0135550101012380.0000000110011000110011390.000000011001100100111011390.000000011001100111010002390.015895100011400.0126760011111400.000000011001100111010012400.0000000110011000110100410.000000011001100100111101410.000000011001100111010102410.0000000110011000110101420.000000011001100100111111420.0191441101112420.0000000110011000110110430.000000011001100101000001430.000000011001100111010112430.02472700100440.000000011001100101000011440.000000011001100111011002440.0000000110011000110111450.0155961000011450.000000011001100111011012450.0000000110011000111000460.000000011001100101000101460.0155921000002460.0000000110011000111001470.000000011001100101000111470.000000011001100111011102470.00587211111011480.000000011001100101001001480.000000011001100111011112480.0000000110011000111010490.022113000001490.000000011001100111100002490.0000000110011000111011500.000000011001100101001011500.0132850100102500.0000000110011000111100510.000000011001100101001101510.000000011001100111100012510.014531011010520.000000011001100101001111520.000000011001100111100102520.0000000110011000111101530.005234111110101530.000000011001100111100112530.0000000110011000111110540.000000011001100101010001540.0186201100112540.0000000110011000111111550.000000011001100101010011550.000000011001100111101002550.018734110100560.000000011001100101010101560.00000001100110011110101570.0190691101101570.00000001100110011110110580.000000011001100101010111580.016113100110590.000000011001100101011001590.00000001100110011110111600.000000011001100101011011600.00000001100110011111000610.0180311011111610.00000001100110011111001620.000000011001100101011101620.014222011000630.000000011001100101011111630.00000001100110011111010640.000000011001100101100001640.00000001100110011111011650.0118680001111650.00000001100110011111100660.000000011001100101100011660.017660101100670.000000011001100101100101670.00000001100110011111101680.000000011001100101100111680.00000001100110011111110690.024749001011690.00000001100110011111111700.000000011001100101101001700.018018101110710.000000011001100101101011710.0000000110011000000000720.000000011001100101101101720.0000000110011000000001730.00624701100101730.0000000110011000000010740.000000011001100101101111740.016758101001750.000000011001100101110001750.0000000110011000000011760.000000011001100101110011760.0000000110011000000100770.0147620110111770.0000000110011000000101780.000000011001100101110101780.012868010000790.000000011001100101110111790.0000000110011000000110800.000000011001100101111001800.0000000110011000000111810.0157061000101810.0000000110011000001000820.000000011001100101111011820.0084701100100830.000000011001100101111101830.0000000110011000001001840.000000011001100101111111840.0000000110011000001010850.0206801110101850.0000000110011000001011860.000000011001100110000001860.017607101011870.000000011001100110000011870.0000000110011000001100880.000000011001100110000101880.0000000110011000001101890.0159051001001890.0000000110011000001110900.000000011001100110000111900.021755111111910.000000011001100110001001910.0000000110011000001111920.000000011001100110001011920.0000000110011000010000930.0149480111011930.0000000110011000010001940.000000011001100110001101940.013438010100950.000000011001100110001111950.0000000110011000010010960.000000011001100110010001960.0000000110011000010011970.0183431100011970.0000000110011000010100980.000000011001100110010011980.014837011100990.000000011001100110010101990.0000000110011000010101由表中的规律可以看出，没4个像素点中只有一个的概率不为0，说明此图进行过量化，实际用到的像素点只有64个。（3）各幅图像信源的编码效率和压缩比编码效率（）:平均每个码符号的最大信息量为logm=log2=1比特/码符号，平均每个信源符号的信息量为H(U)比特/信源符号，平均码长为K码符号/信源符号，则=H(U)K*logm=H(U)K。压缩比（L）：在进行Huffman编码前，每个像素由8个比特位表示，编码后平均每个灰度值的码长为K，所以L=8/K。编码效率和压缩比如下表： 质量因子图像信源熵比特/符号平均码长码符号/信源符号编码效率压缩比图像15.92855.96020.99471.3422图像24.40974.44360.99241.8003图像36.70866.73360.99631.1881（4）用到的主要代码如下：void CImageProcessingDoc:OnImageHuffmancode() int m_Width, m_Height, m_SaveWidth;int i,j;/循环变量double hist256 = 0; /概率分布m_Width = m_pDibInit-GetWidth();m_Height = m_pDibInit-GetHeight();m_SaveWidth = m_pDibInit-GetSaveWidth();hx = 0.0; /信源熵km = 0.0; /平均码长ys = 0.0; /压缩比/计算概率分布for(j=0;jm_Height;j+)for(i=0;im_pDibBitsj*m_SaveWidth+i=histm_pDibInit-m_pDibBitsj*m_SaveWidth + i+1.0/double(m_Width*m_Height);/输出概率统计histFILE*fphist; FILE*fpcode; fphist=fopen(hist.txt,w); fpcode=fopen(code.txt,w); for(i=0;i256;i+) fprintf(fphist,%fn,histi);/计算Huffman编码Huffm huffm(256); /实例化Huffman类huffm.Huffman(hist); /huffm.HuffmanCode(); /进行Huffman编码codetype co; /定义code结构体cofor(i=0;i256;i+)if(histi=0)continue; hx = hx - histi*log(histi)/log(2);/信源熵 co = huffm.codei; /读取Huffman编码 km = km + histi*(256-co.start); /计算平均码长/输出编码code for(i=0;i256;i+)co = huffm.codei; for(j=co.start;j256;j+)fprintf(fpcode,%d,(int)(co.bitsj - 0);fprintf(fpcode,n);ys = 8/km; /压缩比 xl = hx/(km); /编码效率 test = 1; /显示判据 UpdateAllViews(NULL);2、利用图像处理软件Acdsee将“图像1.bmp”、“图像2.bmp”和“图像3.bmp”转换为质量因子为10、50、90的JPG格式图像（共生成9幅JPG图像），比较图像格式转换前后数据量的差异，比较不同品质因素对图像质量的影响； 不同质量的图像数据量如下表： 原始105090图像1302 KB35 KB53 KB142 KB图像2302 KB32 KB49 KB114 KB图像3302 KB52 KB78 KB175 KB 由表可见，三幅图原始大小相同，随着质量因子的减小，数据量也均在减小，但各幅图减小压缩比并不相同。3、数据压缩软件winrar将“图