快速实现两个 8 位数组的协方差

Question

我需要比较大量小尺寸(最高200x200(的类似图像。所以我尝试实现SSIM(结构相似性见 https://en.wikipedia.org/wiki/Structural_similarity(算法。SSIM 需要计算两个 8 位灰度图像的协方差。一个简单的实现如下所示：

float SigmaXY(const uint8_t * x, const uint8_t * y, size_t size, float averageX, float averageY)
{
    float sum = 0;
    for(size_t i = 0; i < size; ++i)
        sum += (x[i] - averageX) * (y[i] - averageY);
    return sum / size;
}

但它的性能很差。所以我希望通过使用 SIMD 或 CUDA 来改进它(我听说可以做到(。不幸的是，我没有这样做的经验。会是什么样子？我该去哪里？

Answer 1

我还有另一个不错的解决方案！

首先我想提一些数学公式：

averageX = Sum(x[i])/size;
averageY = Sum(y[i])/size;

因此：

Sum((x[i] - averageX)*(y[i] - averageY))/size = 
Sum(x[i]*y[i])/size - Sum(x[i]*averageY)/size - 
Sum(averageX*y[i])/size + Sum(averageX*averageY)/size = 
Sum(x[i]*y[i])/size - averageY*Sum(x[i])/size - 
averageX*Sum(y[i])/size + averageX*averageY*Sum(1)/size =   
Sum(x[i]*y[i])/size - averageY*averageX - 
averageX*averageY + averageX*averageY = 
Sum(x[i]*y[i])/size - averageY*averageX;     

它允许修改我们的算法：

float SigmaXY(const uint8_t * x, const uint8_t * y, size_t size, float averageX, float averageY)
{
    uint32_t sum = 0; // If images will have size greater then 256x256 than you have to use uint64_t.
    for(size_t i = 0; i < size; ++i)
        sum += x[i]*y[i];
    return sum / size - averageY*averageX;
} 

只有在此之后，我们才能使用 SIMD(我使用了 SSE2(：

#include <emmintrin.h>
inline __m128i SigmaXY(__m128i x, __m128i y)
{
    __m128i lo = _mm_madd_epi16(_mm_unpacklo_epi8(x, _mm_setzero_si128()), _mm_unpacklo_epi8(y, _mm_setzero_si128()));
    __m128i hi = _mm_madd_epi16(_mm_unpackhi_epi8(y, _mm_setzero_si128()), _mm_unpackhi_epi8(y, _mm_setzero_si128()));
    return _mm_add_epi32(lo, hi);
}
float SigmaXY(const uint8_t * x, const uint8_t * y, size_t size, float averageX, float averageY)
{
    uint32_t sum = 0;
    size_t i = 0, alignedSize = size/16*16;
    if(size >= 16)
    {
        __m128i sums = _mm_setzero_si128();
        for(; i < alignedSize; i += 16)
        {
            __m128i _x = _mm_loadu_si128((__m128i*)(x + i));
            __m128i _y = _mm_loadu_si128((__m128i*)(y + i));
            sums = _mm_add_epi32(sums, SigmaXY(_x, _y));
        }
        uint32_t _sums[4];
        _mm_storeu_si128(_sums, sums);
        sum = _sums[0] + _sums[1] + _sums[2] + _sums[3];
    } 
    for(; i < size; ++i)
        sum += x[i]*y[i];
    return sum / size - averageY*averageX;
}

Answer 2

该算法有一个 SIMD 实现(我使用了 SSE4.1(：

#include <smmintrin.h>
template <int shift> inline __m128 SigmaXY(const __m128i & x, const __m128i & y, __m128 & averageX, __m128 & averageY)
{
    __m128 _x = _mm_cvtepi32_ps(_mm_cvtepu8_epi32(_mm_srli_si128(x, shift)));
    __m128 _y = _mm_cvtepi32_ps(_mm_cvtepu8_epi32(_mm_srli_si128(y, shift)));
    return _mm_mul_ps(_mm_sub_ps(_x, averageX), _mm_sub_ps(_y, averageY))
}
float SigmaXY(const uint8_t * x, const uint8_t * y, size_t size, float averageX, float averageY)
{
    float sum = 0;
    size_t i = 0, alignedSize = size/16*16;
    if(size >= 16)
    {
        __m128 sums = _mm_setzero_ps();
        __m128 avgX = _mm_set1_ps(averageX);
        __m128 avgY = _mm_set1_ps(averageY);
        for(; i < alignedSize; i += 16)
        {
            __m128i _x = _mm_loadu_si128((__m128i*)(x + i));
            __m128i _y = _mm_loadu_si128((__m128i*)(y + i));
            sums = _mm_add_ps(sums, SigmaXY<0>(_x, _y, avgX, avgY);
            sums = _mm_add_ps(sums, SigmaXY<4>(_x, _y, avgX, avgY);
            sums = _mm_add_ps(sums, SigmaXY<8>(_x, _y, avgX, avgY);
            sums = _mm_add_ps(sums, SigmaXY<12>(_x, _y, avgX, avgY);
        }
        float _sums[4];
        _mm_storeu_ps(_sums, sums);
        sum = _sums[0] + _sums[1] + _sums[2] + _sums[3];
    }
    for(; i < size; ++i)
        sum += (x[i] - averageX) * (y[i] - averageY);
    return sum / size;
}

我希望它对您有用。