对rgb和yuv文件分析三个通道的概率分布，并计算各自的熵

实验题目

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

• 思路
  读入down.rgb文件设为fp1，将三个通道分量频率写为red、green、blue三个txt文件存放，熵值由程序直接输出，频率统计结果导入matlab画图得出。
• 代码实现：

#include<stdio.h>
#include<iostream>
#include<cmath>
using namespace std;
int main()
{
	int height=256;
	int width=256;
	int i,j;
	 //R、G、B分量
	unsigned char r[65535]={0};
	unsigned char g[65535]={0};
	unsigned char b[65535]={0};
	//频率分量
	double p_r[256]={0};
	double p_g[256]={0};
	double p_b[256]={0};
	double h_r= 0,h_g= 0,h_b= 0; //熵
	FILE *fp1,*red,*green,*blue;  //设置文件指针,以便打开文件
	fopen_s(&fp1, "D://大三下/数据压缩/down.rgb", "rb");
	fopen_s(&red, "D://大三下/数据压缩/red.txt","w");
    fopen_s(&green, "D://大三下/数据压缩/green.txt","w");
	fopen_s(&blue, "D://大三下/数据压缩/blue.txt","w");
	//依次读取R、G、B
	unsigned char a[196608];
	fread(a, sizeof(unsigned char), 196608, fp1);
	 for (i=0,j=0;i<196608;i=i+3,j++)
   {
      b[j] = a[i];
      g[j] = a[i+1];
      r[j] = a[i+2];
   }
	 //三通道计数
	 for (i = 0; i < 256; i++)
	 {
		 for (int j = 0; j < 65536; j++)
		 {
			 if (int(r[j] == i)) {p_r[i]++;}
			 if (int(g[j] == i)) {p_g[i]++;}
			 if (int(b[j] == i)) {p_b[i]++;}
		 }
	 }
	 //频率
	 for(int i=0;i<256;i++)
   {
      p_r[i]=p_r[i]/double(width*height);
      p_g[i]=p_g[i]/double(width*height);
      p_b[i]=p_b[i]/double(width*height);
   }
	 //计算熵
	  for (int i = 0; i < 256; i++)
 {
  if (p_r[i] != 0) {h_r+=p_r[i]*(log(1/p_r[i])/log(2.0));}
  if (p_g[i] != 0) {h_g+=p_g[i]*(log(1/p_g[i])/log(2.0));}
  if (p_b[i] != 0) {h_b+=p_b[i]*(log(1/p_b[i])/log(2.0));}
 }
 cout << "r熵为" << h_r << endl;
 cout << "g熵为" << h_g << endl;
 cout << "b熵为" << h_b << endl;
	 //由频率数值写三个文件
	fprintf(red,"数值\t概率\n");
	for(int i=0;i<256;i++)
	{
		fprintf(red,"%d\t%f\n",i,p_r[i]);
	}
	fprintf(green,"数值\t概率\n");
	for(int i = 0; i < 256; i++)
	{
		fprintf(green,"%d\t%f\n",i,p_g[i]);
	}
	fprintf(blue,"数值\t概率\n");
	for (int i = 0; i < 256; i++)
	{
		fprintf(blue,"%d\t%f\n",i,p_b[i]);
	}
	fclose(fp1);
	fclose(red);
	fclose(green);
	fclose(blue);
	system("pause");
	return 0;
}

• 代码运行结果

• 

• 三个通道频率分布图
  （红色为R，绿色为G，蓝色为B）
  

down.yuv

• 实验思路
  读入down.rgb文件设为fp2，将三个通道分量频率写为py、pu、pv三个txt文件存放，熵值由程序直接输出，频率统计结果导入matlab画图得出。

• 代码实现
  只需对上述rgb实现代码稍作修改，因为yuv按4:2:0取样，所以u、v各65536/4个。

#include<stdio.h>
#include<iostream>
#include<cmath>
using namespace std;
int main()
{
	int i;
	//Y、U、V分量
	unsigned char y[65535]={0};
	unsigned char u[65535/4]={0};
	unsigned char v[65535/4]={0};
   //频率分量
	double p_y[256]={0};
	double p_u[256]={0};
	double p_v[256]={0};
	//熵
	double h_y= 0.0,h_u= 0.0,h_v= 0.0; 
	FILE *fp2,*py,*pu,*pv;  //设置文件指针,以便打开文件
	fopen_s(&fp2, "D://大三下/数据压缩/down.yuv", "rb");
	fopen_s(&py, "D://大三下/数据压缩/py.txt", "w");
	fopen_s(&pu, "D://大三下/数据压缩/pu.txt", "w");
	fopen_s(&pv, "D://大三下/数据压缩/pv.txt", "w");
	//依次读取Y、U、V
	unsigned char a[98304];
	fread(a, 1, 65535*1.5, fp2);
	for (i = 0; i < 65535 ; i++)
        {
            y[i] = *(a + i);
        }

        for (i =65535; i < 65535 * 1.25; i++)
        {
            u[i-65536] = *(a + i);
        }

        for (i = 65535 * 1.25; i < 65535* 1.5; i++)
        {
            v[i - 81920] = *(a + i);
        }
		//三通道计数
		for (i = 0; i < 65535; i++)
        {
            p_y[y[i]]++;
        }

        for (i = 0; i < (65535/4); i++)
        {
            p_u[u[i]]++;
            p_v[v[i]]++;
        }
		//频率
		for (i = 0; i < 256; i++)
        {
            p_y[i] = p_y[i] / (65535);
            p_u[i] = p_u[i] / (65535/4);
            p_v[i] = p_v[i] / (65535/4);
        }
		//计算熵
		 for (i = 0; i < 256; i++)
        {
            if (p_y[i] != 0) { h_y += -p_y[i] * log(p_y[i]) / log(double(2)); }
            if (p_u[i] != 0) { h_u += -p_u[i] * log(p_u[i]) / log(double(2)); }
            if (p_v[i] != 0) { h_v += -p_v[i] * log(p_v[i]) / log(double(2)); }
        }
		 cout << "y熵为" << h_y << endl;
		 cout << "u熵为" << h_u << endl;
		 cout << "v熵为" << h_v << endl;
		  //由频率数值写三个文件
		  for (int i = 0; i < 256; i++)
        {
            fprintf(py, "%d\t%f\n", i, p_y[i]);
            fprintf(pu, "%d\t%f\n", i, p_u[i]);
            fprintf(pv, "%d\t%f\n", i, p_v[i]);
        }
	fclose(fp2);
	fclose(py);
	fclose(pu);
	fclose(pv);
	system("pause");
	return 0;
}

• 运行结果
  
• 三个通道频率分布图
  （红色为Y，绿色为V，蓝色为U）
  

实验结论

RGB分量的熵大于YUV分量，去相关性更好