两个8位数组协方差的快速实现

Question

我需要比较大量相似的小尺寸图片（最大 200x200）。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：

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;
}

但性能不佳。所以我希望用SIMD或者CUDA来改进一下（听说可以）。不幸的是，我没有这样做的经验。它看起来如何？我要去哪里？

Answer 1

算法有SIMD实现（我用的是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;
}

希望对你有用。

Answer 2

我有另一个很好的解决方案！

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;
}

只有在那之后我们才能使用 SIMD（我使用 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;
}

两个8位数组协方差的快速实现

Fast implementation of covariance of two 8-bit arrays

c++

optimization

sse

image-processing

simd