我正在尝试执行从 YCbCr 到 BGRA 的图像颜色转换(不要问 A 位，好头疼)。

无论如何，这需要尽可能快地执行，所以我使用编译器内部函数编写它以利用 SSE2。这是我第一次涉足 SIMD 领域，我基本上是一个初学者，所以我确信我做的很多事情都是低效的。

事实证明，我执行实际颜色转换的算术代码特别慢，Intel 的 VTune 显示它是一个重大瓶颈。

那么，有什么方法可以加快以下代码的速度吗？它以 32 位、一次 4 个像素完成。我最初尝试以 8 位、一次 16 个像素(如上循环)进行计算，但计算导致整数溢出和转换中断。整个过程，包括 Intel jpeg 解码，对于全高清的单场大约需要 14 毫秒。如果我能将它降低到至少 12 毫秒，最好是 10 毫秒，那就太好了。

非常感谢任何帮助或提示。谢谢!

const __m128i s128_8    = _mm_set1_epi8((char)128);


const int nNumPixels = roi.width * roi.height;

for (int i=0; i<nNumPixels; i+=32)
{
    // Go ahead and prefetch our packed UV Data.
    // As long as the load remains directly next, this saves us time.
    _mm_prefetch((const char*)&pSrc8u[2][i],_MM_HINT_T0); 

    // We need to fetch and blit out our k before we write over it with UV data.
    __m128i sK1 = _mm_load_si128((__m128i*)&pSrc8u[2][i]);
    __m128i sK2 = _mm_load_si128((__m128i*)&pSrc8u[2][i+16]);

    // Using the destination buffer temporarily here so we don't need to waste time doing a memory allocation.
    _mm_store_si128 ((__m128i*)&m_pKBuffer[i],      sK1);
    _mm_store_si128 ((__m128i*)&m_pKBuffer[i+16],   sK2);

    // In theory, this prefetch needs to be some cycles ahead of the first read. It isn't, yet it does appear to save us time. Worth investigating.
    _mm_prefetch((const char*)&pSrc8u[1][i],_MM_HINT_T0); 

    __m128i sUVI1 = _mm_load_si128((__m128i*)&pSrc8u[1][i]);
    __m128i sUVI2 = _mm_load_si128((__m128i*)&pSrc8u[1][i+16]);  

    // Subtract the 128 here ahead of our YCbCr -> BGRA conversion so we can go 16 pixels at a time rather than 4.
    sUVI1 = _mm_sub_epi8(sUVI1, s128_8);
    sUVI2 = _mm_sub_epi8(sUVI2, s128_8);

    // Swizzle and double up UV data from interleaved 8x1 byte blocks into planar
    __m128i sU1 = _mm_unpacklo_epi8(sUVI1, sUVI1);
    __m128i sV1 = _mm_unpackhi_epi8(sUVI1, sUVI1);

    __m128i sU2 = _mm_unpacklo_epi8(sUVI2, sUVI2);  
    __m128i sV2 = _mm_unpackhi_epi8(sUVI2, sUVI2);  

    _mm_store_si128((__m128i*)&pSrc8u[1][i],        sU1);
    _mm_store_si128((__m128i*)&pSrc8u[1][i+16],     sU2); 

    _mm_store_si128((__m128i*)&pSrc8u[2][i],        sV1);
    _mm_store_si128((__m128i*)&pSrc8u[2][i+16],     sV2); 
}

const __m128i s16   = _mm_set1_epi32(16);
const __m128i s299  = _mm_set1_epi32(299);
const __m128i s410  = _mm_set1_epi32(410);
const __m128i s518  = _mm_set1_epi32(518);
const __m128i s101  = _mm_set1_epi32(101);
const __m128i s209  = _mm_set1_epi32(209);

Ipp8u* pDstP = pDst8u;
for (int i=0; i<nNumPixels; i+=4, pDstP+=16)
{
    __m128i sK = _mm_set_epi32(m_pKBuffer[i],           m_pKBuffer[i+1],            m_pKBuffer[i+2],            m_pKBuffer[i+3]);

    __m128i sY = _mm_set_epi32(pSrc8u[0][i],            pSrc8u[0][i+1],             pSrc8u[0][i+2],             pSrc8u[0][i+3]);
    __m128i sU = _mm_set_epi32((char)pSrc8u[1][i],      (char)pSrc8u[1][i+1],       (char)pSrc8u[1][i+2],       (char)pSrc8u[1][i+3]);
    __m128i sV = _mm_set_epi32((char)pSrc8u[2][i],      (char)pSrc8u[2][i+1],       (char)pSrc8u[2][i+2],       (char)pSrc8u[2][i+3]);

    // N.b. - Attempted to do the sub 16 in 8 bits similar to the sub 128 for U and V - however doing it here is quicker
    // as the time saved on the arithmetic is less than the time taken by the additional loads/stores needed in the swizzle loop
    sY = _mm_mullo_epi32(_mm_sub_epi32(sY, s16), s299);

    __m128i sR  = _mm_srli_epi32(_mm_add_epi32(sY,_mm_mullo_epi32(s410, sV)), 8);
    __m128i sG  = _mm_srli_epi32(_mm_sub_epi32(_mm_sub_epi32(sY, _mm_mullo_epi32(s101, sU)),_mm_mullo_epi32(s209, sV)), 8);
    __m128i sB  = _mm_srli_epi32(_mm_add_epi32(sY, _mm_mullo_epi32(s518, sU)), 8);

    //Microsoft's YUV Conversion
    //__m128i sC = _mm_sub_epi32(sY, s16);
    //__m128i sD = _mm_sub_epi32(sU, s128);
    //__m128i sE = _mm_sub_epi32(sV, s128);
    //
    //__m128i sR =  _mm_srli_epi32(_mm_add_epi32(_mm_add_epi32(_mm_mullo_epi32(s298, sC), _mm_mullo_epi32(s409, sE)), s128), 8);
    //__m128i sG    = _mm_srli_epi32(_mm_add_epi32(_mm_sub_epi32(_mm_mullo_epi32(s298, sC), _mm_sub_epi32(_mm_mullo_epi32(s100, sD), _mm_mullo_epi32(s208, sE))), s128), 8);
    //__m128i sB    = _mm_srli_epi32(_mm_add_epi32(_mm_add_epi32(_mm_mullo_epi32(s298, sC), _mm_mullo_epi32(s516, sD)), s128), 8);

    __m128i sKGl = _mm_unpacklo_epi32(sK, sG);
    __m128i sKGh = _mm_unpackhi_epi32(sK, sG);

    __m128i sRBl = _mm_unpacklo_epi32(sR, sB);
    __m128i sRBh = _mm_unpackhi_epi32(sR, sB);

    __m128i sKRGB1 = _mm_unpackhi_epi32(sKGh,sRBh);
    __m128i sKRGB2 = _mm_unpacklo_epi32(sKGh,sRBh);
    __m128i sKRGB3 = _mm_unpackhi_epi32(sKGl,sRBl);
    __m128i sKRGB4 = _mm_unpacklo_epi32(sKGl,sRBl);

    __m128i p1 = _mm_packus_epi16(sKRGB1, sKRGB2);
    __m128i p2 = _mm_packus_epi16(sKRGB3, sKRGB4);

    __m128i po = _mm_packus_epi16(p1, p2);

    _mm_store_si128((__m128i*)pDstP, po);
}

最佳答案

此处您的带宽可能受到限制，因为相对于加载和存储的数量，计算量非常少。

一个建议:摆脱 _mm_prefetch 内在函数 - 它们几乎肯定没有帮助，甚至可能会阻碍更新的 CPU 上的操作(它们已经在自动预取方面做得很好)。

另一个要看的地方:

__m128i sK = _mm_set_epi32(m_pKBuffer[i],           m_pKBuffer[i+1],            m_pKBuffer[i+2],            m_pKBuffer[i+3]);
__m128i sY = _mm_set_epi32(pSrc8u[0][i],            pSrc8u[0][i+1],             pSrc8u[0][i+2],             pSrc8u[0][i+3]);
__m128i sU = _mm_set_epi32((char)pSrc8u[1][i],      (char)pSrc8u[1][i+1],       (char)pSrc8u[1][i+2],       (char)pSrc8u[1][i+3]);
__m128i sV = _mm_set_epi32((char)pSrc8u[2][i],      (char)pSrc8u[2][i+1],       (char)pSrc8u[2][i+2],       (char)pSrc8u[2][i+3]);

这会生成很多不必要的指令 - 你应该在这里使用 _mm_load_xxx 和 _mm_unpackxx_xxx。它将看起来像更多的代码，但它会更有效率。而且您可能应该在循环的每次迭代中处理 16 个像素，而不是 4 个 - 这样您就可以加载一次 8 位值的 vector ，然后根据需要解压缩以将每组 4 个值作为 32 位整数的 vector 。

关于c++ - 加快一些 SSE2 Intrinsics 的颜色转换，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/5031409/