要求：

对down.rgb和down.yuv分析三个通道的概率分布，并计算各自的熵。（编程实现）两个文件的分辨率均为256*256，yuv为4:2:0采样空间，存储格式为：rgb文件按每个像素BGR分量依次存放；YUV格式按照全部像素的Y数据块、U数据块和V数据块依次存放。

*down.rgb

#include<stdio.h>
#include<iostream>
#include<math.h>
using namespace std;
#define A  65536
int main()
{
 //打开,创建文件
 FILE* image, * red, * green,*blue; 
 fopen_s(&image, "C:\\Users\\admin\\Desktop\\数据压缩作业\\down.rgb", "rb");
 fopen_s(&red, "C:\\Users\\admin\\Desktop\\数据压缩作业\\Red.txt", "w");
 fopen_s(&green, "C:\\Users\\admin\\Desktop\\数据压缩作业\\Green.txt", "w");
 fopen_s(&blue, "C:\\Users\\admin\\Desktop\\数据压缩作业\\Blue.txt", "w");
 //定义R、G、B分量
 unsigned char R[A] = { 0 },G[A] = { 0 }, B[A] = { 0 };
 //定义频率分量
 double R_F[256] = { 0 },G_F[256] = { 0 },B_F[256] = { 0 };
 //定义熵
 double R_S = 0,G_S = 0, B_S = 0;
 //分别读取R、G、B三个分量到数组中
 unsigned char sum[256*256*3];
 fread(sum, 1, 256*256*3, image);
 for (int i = 0, j = 0; i < 256*256*3; i = i + 3, j++)
 {
  B[j] = *(sum + i);
  G[j] = *(sum + i + 1);
  R[j] = *(sum + i + 2); 
 }
 //计数三通道各颜色值次数
 for (int i = 0; i < 256; i++)
 {
  for (int j = 0; j < A; j++)
  {
   if (int(R[j] == i)) {R_F[i]++;}
   if (int(G[j] == i)) {G_F[i]++;}
   if (int(B[j] == i)) {B_F[i]++;}
  }
 }
//计算频率
 for (int i = 0; i < 256; i++)
 {
  R_F[i] = R_F[i] / (256 * 256);
  B_F[i] = B_F[i] / (256 * 256);
  G_F[i] = G_F[i] / (256 * 256);
 }
 //将频率写入文件
 fprintf(red, "值\t概率\n");
 for (int i = 0; i < 256; i++)
 {
  fprintf(red, "%d\t%f\n", i, R_F[i]);
 }
 fprintf(green, "值\t概率\n");
 for (int i = 0; i < 256; i++)
 {
  fprintf(green, "%d\t%f\n", i, G_F[i]);
 }
 fprintf(blue, "值\t概率\n");
 for (int i = 0; i < 256; i++)
 {
  fprintf(blue, "%d\t%f\n", i, B_F[i]);
 }
 //计算并输出熵
 for (int i = 0; i < 256; i++)
 {
  if (R_F[i] != 0) {R_S += -R_F[i] * log(R_F[i]) / log(2.0);}
  if (G_F[i] != 0) {G_S += -G_F[i] * log(G_F[i]) / log(2.0);}
  if (B_F[i] != 0) {B_S += -B_F[i] * log(B_F[i]) / log(2.0);}
 }
 cout << "R的熵为" << R_S << endl;
 cout << "G的熵为" << G_S << endl;
 cout << "B的熵为" << B_S << endl;
 fclose(image);
 fclose(red);
 fclose(green);
 fclose(blue);
 return 0;
}
           

运行结果

rgb概率分布

down.yuv

#include<iostream>
#include<Windows.h>
#include<math.h>
using namespace std;

#define Res 256*256//分辨率

int main()
{
    unsigned char Y[Res] = { 0 }, U[Res / 4] = { 0 }, V[Res / 4] = { 0 };    //定义Y、U、V分量
    double Y1[256] = { 0 }, U1[256] = { 0 }, V1[256] = { 0 };   //定义Y、U、V概率分量
    double Y2 = 0, U2 = 0, V2 = 0;    //定义Y、U、V的熵

    FILE* Picture, * PartY, * PartU, * PartV;
    fopen_s(&Picture, "/Users/admin/Desktop/数据压缩作业/down.yuv", "rb");
    fopen_s(&PartY, "/Users/admin/Desktop/数据压缩作业/PartY.txt", "w");
    fopen_s(&PartU, "/Users/admin/Desktop/数据压缩作业/PartU.txt", "w");
    fopen_s(&PartV, "/Users/admin/Desktop/数据压缩作业/PartV.txt", "w");

    if (Picture == 0)
        printf("读取图片失败！");
    else
    {
        //分别读取Y、U、V到数组中
        unsigned char Arr[98304];
        fread(Arr, 1, Res * 1.5, Picture);
        for (int i = 0; i < Res; i++)
        {
            Y[i] = *(Arr + i);
        }

        for (int i = Res; i < Res * 1.25; i++)
        {
            U[i - 65536] = *(Arr + i);
        }

        for (int i = Res * 1.25; i < Res * 1.5; i++)
        {
            V[i - 81920] = *(Arr + i);
        }

        //分别统计Y、U、V三通道的颜色强度级的频数
        for (int i = 0; i < Res; i++)
        {
            Y1[Y[i]]++;
        }

        for (int i = 0; i < (Res / 4); i++)
        {
            U1[U[i]]++;
            V1[V[i]]++;
        }

        //分别计算Y、U、V三通道的256个颜色强度级的概率
        for (int i = 0; i < 256; i++)
        {
            Y1[i] = Y1[i] / (Res);
            U1[i] = U1[i] / (Res / 4);
            V1[i] = V1[i] / (Res / 4);
        }

        //将概率写入文件
        for (int i = 0; i < 256; i++)
        {
            fprintf(PartY, "%d\t%f\n", i, Y1[i]);
            fprintf(PartU, "%d\t%f\n", i, U1[i]);
            fprintf(PartV, "%d\t%f\n", i, V1[i]);
        }

        //计算并输出熵
        for (int i = 0; i < 256; i++)
        {
            if (Y1[i] != 0) { Y2 += -Y1[i] * log(Y1[i]) / log(2*1.0); }
            if (U1[i] != 0) { U2 += -U1[i] * log(U1[i]) / log(2*1.0); }
            if (V1[i] != 0) { V2 += -V1[i] * log(V1[i]) / log(2*1.0); }
        }
        printf("Y的熵为%f\n", Y2);
        printf("U的熵为%f\n", U2);
        printf("V的熵为%f\n", V2);
    }
	system("pause");
    return 0;
}



           

运行结果

yuv概率分布

rgb和yuv文件三个通道的概率分布和各自的熵