Fast calculation of image moments - c++

Question

I have a mask (8-bit gray image) and I need calculate center of region with given index of the mask.
To do this I need calculate moments of first order along axes X and Y for this mask.
Currently I'm using next code:
void GetCenter(const uint8_t * mask, size_t stride, size_t width, size_t height,
uint8_t index, double * centerX, double * centerY)
{
uint64_t sum = 0, sumX = 0, sumY = 0;
for(size_t y = 0; y < height; ++y)
{
for(size_t x = 0; x < width; ++x)
{
if(mask[x] == index)
{
sum++;
sumX += x;
sumY += y;
}
}
mask += stride;
}
*centerX = sum ? (double)sumX/sum : 0;
*centerY = sum ? (double)sumY/sum : 0;
}
And I have a question: Is there any way to improve performance of this algorithm?

Answer 1

There is a way to greatly (more then ten times) improve performance of this algorithm.
To do it you need use SIMD instructions of CPU such as (SSE2, AVX2, Altivec, NEON etc.).
I wrote an example with using of SSE2 instructions (AVX2 code will be similar to it):
const __m128i K_0 = _mm_setzero_si128();
const __m128i K8_1 = _mm_set1_epi8(1);
const __m128i K16_1 = _mm_set1_epi16(1);
const __m128i K16_8 = _mm_set1_epi16(8);
const __m128i K16_I = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7);
inline void AddMoments(const __m128i & mask, const __m128i & x, const __m128i & y,
__m128i & sumX, __m128i & sumY)
{
sumX = _mm_add_epi32(sumX, _mm_madd_epi16(_mm_and_si128(mask, x), K16_1));
sumY = _mm_add_epi32(sumY, _mm_madd_epi16(_mm_and_si128(mask, y), K16_1));
}
inline int ExtractSum(__m128i a)
{
return _mm_cvtsi128_si32(a) + _mm_cvtsi128_si32(_mm_srli_si128(a, 4)) +
_mm_cvtsi128_si32(_mm_srli_si128(a, 8)) + _mm_cvtsi128_si32(_mm_srli_si128(a, 12));
}
void GetCenter(const uint8_t * mask, size_t stride, size_t width, size_t height,
uint8_t index, double * centerX, double * centerY)
{
size_t alignedWidth = width & ~(sizeof(__m128i) - 1);
const __m128i _index = _mm_set1_epi8(index);
uint64_t sum = 0, sumX = 0, sumY = 0;
for(size_t y = 0; y < height; ++y)
{
size_t x = 0;
__m128i _x = K16_I;
__m128i _y = _mm_set1_epi16((short)y);
__m128i _sum = K_0;
__m128i _sumX = K_0;
__m128i _sumY = K_0;
for(; x < alignedWidth; x += sizeof(__m128i))
{
__m128i _mask = _mm_and_si128(_mm_cmpeq_epi8(_mm_loadu_si128((__m128i*)(mask + x)), _index), K8_1);
_sum = _mm_add_epi64(_sum, _mm_sad_epu8(_mask, K_0));
AddMoments(_mm_cmpeq_epi16(_mm_unpacklo_epi8(_mask, K_0), K16_1), _x, _y, _sumX, _sumY);
_x = _mm_add_epi16(_x, K16_8);
AddMoments(_mm_cmpeq_epi16(_mm_unpackhi_epi8(_mask, K_0), K16_1), _x, _y, _sumX, _sumY);
_x = _mm_add_epi16(_x, K16_8);
}
sum += ExtractSum(_sum);
sumX += ExtractSum(_sumX);
sumY += ExtractSum(_sumY);
for(; x < width; ++x)
{
if(mask[x] == index)
{
sum++;
sumX += x;
sumY += y;
}
}
mask += stride;
}
*centerX = sum ? (double)sumX/sum : 0;
*centerY = sum ? (double)sumY/sum : 0;
}
P.S. There is a more simple and cross platform way to improve performance with using of external library (http://simd.sourceforge.net/):
void GetCenter(const uint8_t * mask, size_t stride, size_t width, size_t height,
uint8_t index, double * centerX, double * centerY)
{
uint64_t sum, sumX, sumY, sumXX, sumXY, sumYY;
::SimdGetMoments(mask, stride, width, height, index,
&sum, &sumX, &sumY, &sumXX, &sumXY, &sumYY);
*centerX = sum ? (double)sumX/sum : 0;
*centerY = sum ? (double)sumY/sum : 0;
}
An implementation with using of _mm_movemask_epi8 and 8-bit lookup tables:
uint8_t g_sum[1 << 8], g_sumX[1 << 8];
bool Init()
{
for(int i = 0, n = 1 << 8; i < n; ++i)
{
g_sum[i] = 0;
g_sumX[i] = 0;
for(int j = 0; j < 8; ++j)
{
g_sum[i] += (i >> j) & 1;
g_sumX[i] += ((i >> j) & 1)*j;
}
}
return true;
}
bool g_inited = Init();
inline void AddMoments(uint8_t mask, size_t x, size_t y,
uint64_t & sum, uint64_t & sumX, uint64_t & sumY)
{
int value = g_sum[mask];
sum += value;
sumX += x * value + g_sumX[mask];
sumY += y * value;
}
void GetCenter(const uint8_t * mask, size_t stride, size_t width, size_t height,
uint8_t index, double * centerX, double * centerY)
{
size_t alignedWidth = width & ~(sizeof(__m128i) - 1);
const __m128i _index = _mm_set1_epi8(index);
union PackedValue
{
uint8_t u8[4];
uint16_t u16[2];
uint32_t u32;
} _mask;
uint64_t sum = 0, sumX = 0, sumY = 0;
for(size_t y = 0; y < height; ++y)
{
size_t x = 0;
for(; x < alignedWidth; x += sizeof(__m128i))
{
_mask.u32 = _mm_movemask_epi8(_mm_cmpeq_epi8(
_mm_loadu_si128((__m128i*)(mask + x)), _index));
AddMoments(_mask.u8[0], x, y, sum, sumX, sumY);
AddMoments(_mask.u8[1], x + 8, y, sum, sumX, sumY);
}
for(; x < width; ++x)
{
if(mask[x] == index)
{
sum++;
sumX += x;
sumY += y;
}
}
mask += stride;
}
*centerX = sum ? (double)sumX/sum : 0;
*centerY = sum ? (double)sumY/sum : 0;
}
An implementation with using of _mm_movemask_epi8 and 16-bit lookup tables:
uint16_t g_sum[1 << 16], g_sumX[1 << 16];
bool Init()
{
for(int i = 0, n = 1 << 16; i < n; ++i)
{
g_sum[i] = 0;
g_sumX[i] = 0;
for(int j = 0; j < 16; ++j)
{
g_sum[i] += (i >> j) & 1;
g_sumX[i] += ((i >> j) & 1)*j;
}
}
return true;
}
bool g_inited = Init();
inline void AddMoments(uint16_t mask, size_t x, size_t y,
uint64_t & sum, uint64_t & sumX, uint64_t & sumY)
{
int value = g_sum[mask];
sum += value;
sumX += x * value + g_sumX[mask];
sumY += y * value;
}
void GetCenter(const uint8_t * mask, size_t stride, size_t width, size_t height,
uint8_t index, double * centerX, double * centerY)
{
size_t alignedWidth = width & ~(sizeof(__m128i) - 1);
const __m128i _index = _mm_set1_epi8(index);
union PackedValue
{
uint8_t u8[4];
uint16_t u16[2];
uint32_t u32;
} _mask;
uint64_t sum = 0, sumX = 0, sumY = 0;
for(size_t y = 0; y < height; ++y)
{
size_t x = 0;
for(; x < alignedWidth; x += sizeof(__m128i))
{
_mask.u32 = _mm_movemask_epi8(_mm_cmpeq_epi8(
_mm_loadu_si128((__m128i*)(mask + x)), _index));
AddMoments(_mask.u16[0], x, y, sum, sumX, sumY);
}
for(; x < width; ++x)
{
if(mask[x] == index)
{
sum++;
sumX += x;
sumY += y;
}
}
mask += stride;
}
*centerX = sum ? (double)sumX/sum : 0;
*centerY = sum ? (double)sumY/sum : 0;
}
Performance comparison for 1920x1080 image:
Base version: 8.261 ms;
1-st optimization:0.363 ms (in 22 times faster);
2-nd optimization:0.280 ms (in 29 times faster);
3-rd optimization:0.299 ms (in 27 times faster);
4-th optimization:0.325 ms (in 25 times faster);
As you can see above the code with using of 8-bit lookup tables has better performance then the code with using of 16-bit lookup tables. But anyway external library is better though it performs additional calculations of the second order moments.

Answer 2

Another acceleration technique is by run-length coding.
You can decompose the rows in horizontal runs where the mask is active. You can detect the runs on the fly, or precompute them and store the image in that form, if that makes sense.
Then a run can be accumulated as a whole. Let a run start from (X, Y) and have length L, then use
Sum+= L;
SumX+= (2 * X + L + 1) * L;
SumY+= Y * L;
In the end, divide SumX by 2.
The longer the runs, the more effective the trick is.
Using SSE2 or later, you try with the instruction PMOVMSKB—Move Byte Mask.
First compare 16 mask pixels to the (replicated) index value to get 16 comparison results. Then pack these to a 16 bits number, using the magical instruction.
Then, using two precomputed lookup tables, perform the accumulations in scalar mode.
One lookup table gives you the count of active mask pixels, and the other gives you the sum of active mask pixels weighted by their abscissa, i.e the X moment.
Something like
int PackedValue= _mm_movemask_epi8(_mm_cmpeq_epi8(_mm_loadu_si128((__m128i*)&Mask[X]), ReplicatedIndex));
Sum+= Count[PackedValue];
SumX+= X * Count[PackedValue] + MomentX[PackedValue];
SumY+= Y * Count[PackedValue];
Depending on the amount of memory you agree to spend, the lookup tables can have byte indexes (256 entries, use the table twice) or word indexes (65536 entries). In both cases, the count and moment values fit in a single byte (1 to 8/16 and 0 to 28/120 respectively).
AVX implementation is also possible, packing 32 pixels at a time. Lookup tables with doubleword indexes seem unreasonable, though. :-)