【AI PC端算法优化】三，深入优化RGB转灰度图算法

女爷i 2023-07-24 11:48 4阅读 0赞

# 0. 目录 #

*  前言
 *  RGB2GRAY最简单实现
 *  RGB转GRAY优化第一版（float->INT）
 *  RGB转GRAY优化第二版（手动4路并行）
 *  RGB转GRAY优化第三版（OpenMP4线程）
 *  RGB转GRAY优化第四版（SSE优化，一次处理12个像素）
 *  RGB转GRAY优化第五版（SSE优化，一次处理15个像素）
 *  RGB转GRAY优化第六版（AVX2优化，一次处理10个像素）
 *  RGB转GRAY优化第七版（AVX2优化+std::async）

# 1. 前言 #

前几天发了一篇一步步优化RGB转灰度图算法，但实验做的并不完善，在上次的基础上我又补充了一些优化技巧，相对于传统实现将RGB转灰度图算法可以加速到近5倍左右。所以，这篇文章再次将所有涉及到的优化方法进行汇总，SSE优化相关的原理上一节已经讲得很清楚了，这里就不会再展开了，感兴趣可以查看上篇文章。[【AI PC端算法优化】一，一步步优化RGB转灰度图算法][AI PC_RGB] 这一节的速度测试环境为：

**测试CPU型号：Intel® Core™ i7-6700 CPU @ 3.40GHz**

# 2. RGB转GRAY最简单实现 #

//origin
    void RGB2Y(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride) {
    	for (int Y = 0; Y < Height; Y++) {
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    		for (int X = 0; X < Width; X++, LinePS += 3) {
    			LinePD[X] = int(0.114 * LinePS[0] + 0.587 * LinePS[1] + 0.299 * LinePS[2]);
    		}
    	}
    }

耗时信息如下：

# 3. RGB转GRAY优化第一版 #

直接计算复杂度较高，考虑优化可以将小数转为整数，除法变为移位，乘法也变为移位，但是这种方法也会带来一定的精度损失，我们可以根据实际情况选择需要保留的精度位数。下面给出不同精度(2-20位)的计算公式：

Gray = (R*1 + G*2 + B*1) >> 2
    
    Gray= (R*2 + G*5 + B*1) >> 3
    
    Gray= (R*4 + G*10 + B*2) >> 4
    
    Gray = (R*9 + G*19 + B*4) >> 5
    
    Gray = (R*19 + G*37 + B*8) >> 6
    
    Gray= (R*38 + G*75 + B*15) >> 7
    
    Gray= (R*76 + G*150 + B*30) >> 8
    
    Gray = (R*153 + G*300 + B*59) >> 9
    
    Gray = (R*306 + G*601 + B*117) >> 10
    
    Gray = (R*612 + G*1202 + B*234) >> 11
    
    Gray = (R*1224 + G*2405 + B*467) >> 12
    
    Gray= (R*2449 + G*4809 + B*934) >> 13
    
    Gray= (R*4898 + G*9618 + B*1868) >> 14
    
    Gray = (R*9797 + G*19235 + B*3736) >> 15
    
    Gray = (R*19595 + G*38469 + B*7472) >> 16
    
    Gray = (R*39190 + G*76939 + B*14943) >> 17
    
    Gray = (R*78381 + G*153878 + B*29885) >> 18
    
    Gray =(R*156762 + G*307757 + B*59769) >> 19
    
    Gray= (R*313524 + G*615514 + B*119538) >> 20

下面测试一下保留8位精度的代码实现和速度测试：

//int
    void RGB2Y_1(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride) {
    	const int B_WT = int(0.114 * 256 + 0.5);
    	const int G_WT = int(0.587 * 256 + 0.5);
    	const int R_WT = 256 - B_WT - G_WT;
    	for (int Y = 0; Y < Height; Y++) {
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    		for (int X = 0; X < Width; X++, LinePS += 3) {
    			LinePD[X] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
    		}
    	}
    }

这一优化将速度加速了10ms，还是比较可观的。

# 4. RGB转GRAY优化第二版 #

在第一版优化的基础上，使用**4路并行**，然后我们看看有没有进一步的加速效果。代码实现和速度测试如下：

//4路并行
    void RGB2Y_2(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride) {
    	const int B_WT = int(0.114 * 256 + 0.5);
    	const int G_WT = int(0.587 * 256 + 0.5);
    	const int R_WT = 256 - B_WT - G_WT; // int(0.299 * 256 + 0.5)
    	for (int Y = 0; Y < Height; Y++) {
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    		int X = 0;
    		for (; X < Width - 4; X += 4, LinePS += 12) {
    			LinePD[X + 0] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
    			LinePD[X + 1] = (B_WT * LinePS[3] + G_WT * LinePS[4] + R_WT * LinePS[5]) >> 8;
    			LinePD[X + 2] = (B_WT * LinePS[6] + G_WT * LinePS[7] + R_WT * LinePS[8]) >> 8;
    			LinePD[X + 3] = (B_WT * LinePS[9] + G_WT * LinePS[10] + R_WT * LinePS[11]) >> 8;
    		}
    		for (; X < Width; X++, LinePS += 3) {
    			LinePD[X] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
    		}
    	}
    }

这里测试的时候并没有加速效果，反而还慢了很多。

# 5. RGB转GRAY优化第三版 #

利用OpenMP进行4线程加速，代码如下：

//openmp
    void RGB2Y_3(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride) {
    	const int B_WT = int(0.114 * 256 + 0.5);
    	const int G_WT = int(0.587 * 256 + 0.5);
    	const int R_WT = 256 - B_WT - G_WT;
    	for (int Y = 0; Y < Height; Y++) {
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    #pragma omp parallel for num_threads(4)
    		for (int X = 0; X < Width; X++) {
    			LinePD[X] = (B_WT * LinePS[0 + X*3] + G_WT * LinePS[1 + X*3] + R_WT * LinePS[2 + X*3]) >> 8;
    		}
    	}
    }

可以看到使用OpenMP进行多线程加速，算法的速度和第一版的速度也是差不多的，没有明显的加速效果。

# 6. RGB转GRAY优化第四版 #

这一次优化就是上篇推文讲的SSE优化，原理就不再多说了，可以去查看上篇推文，代码实现如下：

//sse 一次处理12个
    void RGB2Y_4(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride) {
    	const int B_WT = int(0.114 * 256 + 0.5);
    	const int G_WT = int(0.587 * 256 + 0.5);
    	const int R_WT = 256 - B_WT - G_WT; // int(0.299 * 256 + 0.5)
    
    	for (int Y = 0; Y < Height; Y++) {
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    		int X = 0;
    		for (; X < Width - 12; X += 12, LinePS += 36) {
    			__m128i p1aL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 0))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT)); //1
    			__m128i p2aL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 1))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT)); //2
    			__m128i p3aL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 2))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT)); //3
    
    			__m128i p1aH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 8))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));//4
    			__m128i p2aH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 9))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));//5
    			__m128i p3aH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 10))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));//6
    
    			__m128i p1bL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 18))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));//7
    			__m128i p2bL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 19))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));//8
    			__m128i p3bL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 20))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));//9
    
    			__m128i p1bH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 26))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));//10
    			__m128i p2bH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 27))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));//11
    			__m128i p3bH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 28))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));//12
    
    			__m128i sumaL = _mm_add_epi16(p3aL, _mm_add_epi16(p1aL, p2aL));//13
    			__m128i sumaH = _mm_add_epi16(p3aH, _mm_add_epi16(p1aH, p2aH));//14
    			__m128i sumbL = _mm_add_epi16(p3bL, _mm_add_epi16(p1bL, p2bL));//15
    			__m128i sumbH = _mm_add_epi16(p3bH, _mm_add_epi16(p1bH, p2bH));//16
    			__m128i sclaL = _mm_srli_epi16(sumaL, 8);//17
    			__m128i sclaH = _mm_srli_epi16(sumaH, 8);//18
    			__m128i sclbL = _mm_srli_epi16(sumbL, 8);//19
    			__m128i sclbH = _mm_srli_epi16(sumbH, 8);//20
    			__m128i shftaL = _mm_shuffle_epi8(sclaL, _mm_setr_epi8(0, 6, 12, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1));//21
    			__m128i shftaH = _mm_shuffle_epi8(sclaH, _mm_setr_epi8(-1, -1, -1, 18, 24, 30, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1));//22
    			__m128i shftbL = _mm_shuffle_epi8(sclbL, _mm_setr_epi8(-1, -1, -1, -1, -1, -1, 0, 6, 12, -1, -1, -1, -1, -1, -1, -1));//23
    			__m128i shftbH = _mm_shuffle_epi8(sclbH, _mm_setr_epi8(-1, -1, -1, -1, -1, -1, -1, -1, -1, 18, 24, 30, -1, -1, -1, -1));//24
    			__m128i accumL = _mm_or_si128(shftaL, shftbL);//25
    			__m128i accumH = _mm_or_si128(shftaH, shftbH);//26
    			__m128i h3 = _mm_or_si128(accumL, accumH);//27
    													  //__m128i h3 = _mm_blendv_epi8(accumL, accumH, _mm_setr_epi8(0, 0, 0, -1, -1, -1, 0, 0, 0, -1, -1, -1, 1, 1, 1, 1));
    			_mm_storeu_si128((__m128i *)(LinePD + X), h3);
    		}
    		for (; X < Width; X++, LinePS += 3) {
    			LinePD[X] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
    		}
    	}
    }

# 7. RGB转灰度图优化第五版 #

在上篇推文中提到 `_mm_storeu_si128`把处理的结果写入到目标内存中，会多写了**4个字节**的内存数据（128 - 12 \* 8），但是我们后面又会把他们重新覆盖掉，这就造成了重复计算，因此简单修改将一次处理12个像素变成一次处理15个像素，**那么重复计算的数量就会下降**，代码如下：

//sse 一次处理15个
    void RGB2Y_5(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride) {
    	const int B_WT = int(0.114 * 256 + 0.5);
    	const int G_WT = int(0.587 * 256 + 0.5);
    	const int R_WT = 256 - B_WT - G_WT; // int(0.299 * 256 + 0.5)
    
    	for (int Y = 0; Y < Height; Y++) {
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    		int X = 0;
    		for (; X < Width - 15; X += 15, LinePS += 45)
    		{
    			__m128i p1aL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 0))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT)); //1
    			__m128i p2aL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 1))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT)); //2
    			__m128i p3aL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 2))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT)); //3
    
    			__m128i p1aH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 8))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));
    			__m128i p2aH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 9))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));
    			__m128i p3aH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 10))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));
    
    			__m128i p1bL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 18))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));
    			__m128i p2bL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 19))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));
    			__m128i p3bL = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 20))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));
    
    			__m128i p1bH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 26))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));
    			__m128i p2bH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 27))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));
    			__m128i p3bH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 28))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));
    
    			__m128i p1cH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 36))), _mm_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT));
    			__m128i p2cH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 37))), _mm_setr_epi16(G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT));
    			__m128i p3cH = _mm_mullo_epi16(_mm_cvtepu8_epi16(_mm_loadu_si128((__m128i *)(LinePS + 38))), _mm_setr_epi16(R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT));
    
    			__m128i sumaL = _mm_add_epi16(p3aL, _mm_add_epi16(p1aL, p2aL));
    			__m128i sumaH = _mm_add_epi16(p3aH, _mm_add_epi16(p1aH, p2aH));
    			__m128i sumbL = _mm_add_epi16(p3bL, _mm_add_epi16(p1bL, p2bL));
    			__m128i sumbH = _mm_add_epi16(p3bH, _mm_add_epi16(p1bH, p2bH));
    			__m128i sumcH = _mm_add_epi16(p3cH, _mm_add_epi16(p1cH, p2cH));
    
    			__m128i sclaL = _mm_srli_epi16(sumaL, 8);
    			__m128i sclaH = _mm_srli_epi16(sumaH, 8);
    			__m128i sclbL = _mm_srli_epi16(sumbL, 8);
    			__m128i sclbH = _mm_srli_epi16(sumbH, 8);
    			__m128i sclcH = _mm_srli_epi16(sumcH, 8);
    
    			__m128i shftaL = _mm_shuffle_epi8(sclaL, _mm_setr_epi8(0, 6, 12, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1));
    			__m128i shftaH = _mm_shuffle_epi8(sclaH, _mm_setr_epi8(-1, -1, -1, 2, 8, 14, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1));
    			__m128i shftbL = _mm_shuffle_epi8(sclbL, _mm_setr_epi8(-1, -1, -1, -1, -1, -1, 0, 6, 12, -1, -1, -1, -1, -1, -1, -1));
    			__m128i shftbH = _mm_shuffle_epi8(sclbH, _mm_setr_epi8(-1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 8, 14, -1, -1, -1, -1));
    			__m128i shftcH = _mm_shuffle_epi8(sclcH, _mm_setr_epi8(-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 6, 12, -1));
    			__m128i accumL = _mm_or_si128(shftaL, shftbL);
    			__m128i accumH = _mm_or_si128(shftaH, shftbH);
    			__m128i h3 = _mm_or_si128(accumL, accumH);
    			h3 = _mm_or_si128(h3, shftcH);
    			_mm_storeu_si128((__m128i *)(LinePD + X), h3);
    		}
    		for (; X < Width; X++, LinePS += 3) {
    			LinePD[X] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
    		}
    	}
    }

<table> 
 <thead> 
  <tr> 
   <th>分辨率</th> 
   <th>优化</th> 
   <th>循环次数</th> 
   <th>速度</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>4032x3024</td> 
   <td>原始实现</td> 
   <td>1000</td> 
   <td>12.139ms</td> 
  </tr> 
  <tr> 
   <td>4032x3024</td> 
   <td>第一版优化（float-&gt;INT）</td> 
   <td>1000</td> 
   <td>7.629ms</td> 
  </tr> 
  <tr> 
   <td>4032x3024</td> 
   <td>第二版优化（手动4路并行）</td> 
   <td>1000</td> 
   <td>10.528ms</td> 
  </tr> 
  <tr> 
   <td>4032x3024</td> 
   <td>第三版优化（OpenMP4线程）</td> 
   <td>1000</td> 
   <td>7.632ms</td> 
  </tr> 
  <tr> 
   <td>4032x3024</td> 
   <td>第四版优化（SSE优化，一次处理12个像素）</td> 
   <td>1000</td> 
   <td>5.579ms</td> 
  </tr> 
  <tr> 
   <td>4032x3024</td> 
   <td>第五版优化（SSE优化，一次处理15个像素）</td> 
   <td>1000</td> 
   <td>5.843ms</td> 
  </tr> 
 </tbody> 
</table>

很遗憾并没有取得什么提升，考虑原因可能是因为图片的宽度除以12和除以15这两个直拉不开明显的数量级差距，导致重复计算不是特别多，在速度上体现不出来。

# 8. RGB转灰度图优化第六版 #

这一版即是使用AVX/AVX2指令集来优化，我们知道AVX寄存器可以一次处理256位也就是32个uchar，因此使用AVX来进行优化获取能带来性能提升。参考Github工程`https://github.com/komrad36/RGB2Y/blob/master/RGB2Y.h`将代码重写如下，注意这里的原理已经在注释里面标明了，如果你有疑惑可以到官方网站(`https://software.intel.com/sites/landingpage/IntrinsicsGuide/#techs=AVX&expand=4155`)查询指令集的对应操作或者联系我为你解答疑惑。

在AVX这一节的优化中，**梁德澎作者对我理解这段代码提供了巨大的帮助，在此特别感谢**。

AVX2指令集实现的代码如下：

// AVX2
    constexpr double B_WEIGHT = 0.114;
    constexpr double G_WEIGHT = 0.587;
    constexpr double R_WEIGHT = 0.299;
    constexpr uint16_t B_WT = static_cast<uint16_t>(32768.0 * B_WEIGHT + 0.5);
    constexpr uint16_t G_WT = static_cast<uint16_t>(32768.0 * G_WEIGHT + 0.5);
    constexpr uint16_t R_WT = static_cast<uint16_t>(32768.0 * R_WEIGHT + 0.5);
    static const __m256i weight_vec = _mm256_setr_epi16(B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT, G_WT, R_WT, B_WT);
    
    void  _RGB2Y(unsigned char* Src, const int32_t Width, const int32_t start_row, const int32_t thread_stride, const int32_t Stride, unsigned char* Dest)
    {
    	for (int Y = start_row; Y < start_row + thread_stride; Y++)
    	{
    		//Sleep(1);
    		unsigned char *LinePS = Src + Y * Stride;
    		unsigned char *LinePD = Dest + Y * Width;
    		int X = 0;
    		for (; X < Width - 10; X += 10, LinePS += 30)
    		{
    			//B1 G1 R1 B2 G2 R2 B3 G3 R3 B4 G4 R4 B5 G5 R5 B6 
    			__m256i temp = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i*)(LinePS + 0)));
    			__m256i in1 = _mm256_mulhrs_epi16(temp, weight_vec);
    
    			//B6 G6 R6 B7 G7 R7 B8 G8 R8 B9 G9 R9 B10 G10 R10 B11
    			temp = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i*)(LinePS + 15)));
    			__m256i in2 = _mm256_mulhrs_epi16(temp, weight_vec);
    
    
    			//0  1  2  3   4  5  6  7  8  9  10 11 12 13 14 15    16 17 18 19 20 21 22 23 24 25 26 27 28   29 30  31       
    			//B1 G1 R1 B2 G2 R2 B3 G3  B6 G6 R6 B7 G7 R7 B8 G8    R3 B4 G4 R4 B5 G5 R5 B6 R8 B9 G9 R9 B10 G10 R10 B11
    			__m256i mul = _mm256_packus_epi16(in1, in2);
    
    			__m256i b1 = _mm256_shuffle_epi8(mul, _mm256_setr_epi8(
    				//  B1 B2 B3 -1, -1, -1  B7  B8  -1, -1, -1, -1, -1, -1, -1, -1,
    				0, 3, 6, -1, -1, -1, 11, 14, -1, -1, -1, -1, -1, -1, -1, -1,
    
    				//  -1, -1, -1, B4 B5 B6 -1, -1  B9 B10 -1, -1, -1, -1, -1, -1
    				-1, -1, -1, 1, 4, 7, -1, -1, 9, 12, -1, -1, -1, -1, -1, -1));
    
    			__m256i g1 = _mm256_shuffle_epi8(mul, _mm256_setr_epi8(
    
    				// G1 G2 G3 -1, -1  G6 G7  G8  -1, -1, -1, -1, -1, -1, -1, -1, 
    				1, 4, 7, -1, -1, 9, 12, 15, -1, -1, -1, -1, -1, -1, -1, -1,
    
    				//  -1, -1, -1  G4 G5 -1, -1, -1  G9  G10 -1, -1, -1, -1, -1, -1	
    				-1, -1, -1, 2, 5, -1, -1, -1, 10, 13, -1, -1, -1, -1, -1, -1));
    
    			__m256i r1 = _mm256_shuffle_epi8(mul, _mm256_setr_epi8(
    
    				//  R1 R2 -1  -1  -1  R6  R7  -1, -1, -1, -1, -1, -1, -1, -1, -1,	
    				2, 5, -1, -1, -1, 10, 13, -1, -1, -1, -1, -1, -1, -1, -1, -1,
    
    				//  -1, -1, R3 R4 R5 -1, -1, R8 R9  R10 -1, -1, -1, -1, -1, -1
    				-1, -1, 0, 3, 6, -1, -1, 8, 11, 14, -1, -1, -1, -1, -1, -1));
    
    
    
    			// B1+G1+R1  B2+G2+R2 B3+G3  0 0 G6+R6  B7+G7+R7 B8+G8 0 0 0 0 0 0 0 0 0 0 R3 B4+G4+R4 B5+G5+R5 B6 0 R8 B9+G9+R9 B10+G10+R10 0 0 0 0 0 0
    
    			__m256i accum = _mm256_adds_epu8(r1, _mm256_adds_epu8(b1, g1));
    
    
    			// _mm256_castsi256_si128(accum)
    			// B1+G1+R1  B2+G2+R2 B3+G3  0 0 G6+R6  B7+G7+R7 B8+G8 0 0 0 0 0 0 0 0
    
    			// _mm256_extracti128_si256(accum, 1)
    			// 0 0 R3 B4+G4+R4 B5+G5+R5 B6 0 R8 B9+G9+R9 B10+G10+R10 0 0 0 0 0 0
    
    			__m128i h3 = _mm_adds_epu8(_mm256_castsi256_si128(accum), _mm256_extracti128_si256(accum, 1));
    
    			_mm_storeu_si128((__m128i *)(LinePD + X), h3);
    		}
    		for (; X < Width; X++, LinePS += 3) {
    			int tmpB = (B_WT * LinePS[0]) >> 14 + 1;
    			tmpB = max(min(255, tmpB), 0);
    
    			int tmpG = (G_WT * LinePS[1]) >> 14 + 1;
    			tmpG = max(min(255, tmpG), 0);
    
    			int tmpR = (R_WT * LinePS[2]) >> 14 + 1;
    			tmpR = max(min(255, tmpR), 0);
    
    			int tmp = tmpB + tmpG + tmpR;
    			LinePD[X] = max(min(255, tmp), 0);
    		}
    	}
    }
    
    //avx2 
    void RGB2Y_6(unsigned char *Src, unsigned char *Dest, int width, int height, int stride)
    {
    	_RGB2Y(Src, width, 0, height, stride, Dest);
    }

相比于SSE优化提高了2.xms，提升幅度还是比较大的。

# 9. RGB转灰度图优化第七版 #

在AVX2优化的基础上如果我们将多线程也加入进来，是否会获得提升呢？这里并非使用OpenMP而是使用C++中的std::async异步并行编程创建多个线程来执行整个任务，代码实现如下：

//avx2 + std::async异步编程
    void RGB2Y_7(unsigned char *Src, unsigned char *Dest, int width, int height, int stride) {
    	const int32_t hw_concur = std::min(height >> 4, static_cast<int32_t>(std::thread::hardware_concurrency()));
    	std::vector<std::future<void>> fut(hw_concur);
    	const int thread_stride = (height - 1) / hw_concur + 1;
    	int i = 0, start = 0;
    	for (; i < std::min(height, hw_concur); i++, start += thread_stride)
    	{
    		fut[i] = std::async(std::launch::async, _RGB2Y, Src, width, start, thread_stride, stride, Dest);
    	}
    	for (int j = 0; j < i; ++j)
    		fut[j].wait();
    }

速度测试结果如下：

可以看到使用异步并行以及AVX优化后，我们已经将原始算法的速度优化了**接近5倍**，在PC端优化RGB转灰度图算法我能想到和调研到的一些相关算法大概就这么多了，如果还有其它相关的想法或者方法可以在评论区留言讨论。

# 10. 总结 #

--------------------

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[AI PC_RGB]: https://mp.weixin.qq.com/s/itvuHfLwtgyE43LyoQc8UA
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