Applying Matrix To Image, Seeking Performance Improvements - c++

Question

Edited: Working on Windows platform.
Problem: Less of a problem, more about advise. I'm currently not incredibly versed in low-level program, but I am attempting to optimize the code below in an attempt to increase the performance of my overall code. This application depends on extremely high speed image processing.
Current Performance: On my computer, this currently computes at about 4-6ms for a 512x512 image. I'm trying to cut that in half if possible.
Limitations: Due to this projects massive size, fundamental changes to the application are very difficult to do, so things such as porting to DirectX or other GPU methods isn't much of an option. The project currently works, I'm simply trying to figure out how to make it work faster.
Specific information about my use for this: Images going into this method are always going to be exactly square and some increment of 128. (Most likely 512 x 512) and they will always come out the same size. Other than that, there is not much else to it. The matrix is calculated somewhere else, so this is just the applying of the matrix to my image. The original image and the new image are both being used, so copying the image is necessary.
Here is my current implementation:
void ReprojectRectangle( double *mpProjMatrix, unsigned char *pDstScan0, unsigned char *pSrcScan0,
int NewBitmapDataStride, int SrcBitmapDataStride, int YOffset, double InversedAspect, int RectX, int RectY, int RectW, int RectH)
{
int i, j;
double Xnorm, Ynorm;
double Ynorm_X_ProjMatrix4, Ynorm_X_ProjMatrix5, Ynorm_X_ProjMatrix7;;
double SrcX, SrcY, T;
int SrcXnt, SrcYnt;
int SrcXec, SrcYec, SrcYnvDec;
unsigned char *pNewPtr, *pSrcPtr1, *pSrcPtr2, *pSrcPtr3, *pSrcPtr4;
int RectX2, RectY2;
/* Compensate (or re-center) the Y-coordinate regarding the aspect ratio */
RectY -= YOffset;
/* Compute the second point of the rectangle for the loops */
RectX2 = RectX + RectW;
RectY2 = RectY + RectH;
/* Clamp values (be careful with aspect ratio */
if (RectY < 0) RectY = 0;
if (RectY2 < 0) RectY2 = 0;
if ((double)RectY > (InversedAspect * 512.0)) RectY = (int)(InversedAspect * 512.0);
if ((double)RectY2 > (InversedAspect * 512.0)) RectY2 = (int)(InversedAspect * 512.0);
/* Iterate through each pixel of the scaled re-Proj */
for (i=RectY; i<RectY2; i++)
{
/* Normalize Y-coordinate and take the ratio into account */
Ynorm = InversedAspect - (double)i / 512.0;
/* Pre-compute some matrix coefficients */
Ynorm_X_ProjMatrix4 = Ynorm * mpProjMatrix[4] + mpProjMatrix[12];
Ynorm_X_ProjMatrix5 = Ynorm * mpProjMatrix[5] + mpProjMatrix[13];
Ynorm_X_ProjMatrix7 = Ynorm * mpProjMatrix[7] + mpProjMatrix[15];
for (j=RectX; j<RectX2; j++)
{
/* Get a pointer to the pixel on (i,j) */
pNewPtr = pDstScan0 + ((i+YOffset) * NewBitmapDataStride) + j;
/* Normalize X-coordinates */
Xnorm = (double)j / 512.0;
/* Compute the corresponding coordinates in the source image, before Proj and normalize source coordinates*/
T = (Xnorm * mpProjMatrix[3] + Ynorm_X_ProjMatrix7);
SrcY = (Xnorm * mpProjMatrix[0] + Ynorm_X_ProjMatrix4)/T;
SrcX = (Xnorm * mpProjMatrix[1] + Ynorm_X_ProjMatrix5)/T;
// Compute the integer and decimal values of the coordinates in the sources image
SrcXnt = (int) SrcX;
SrcYnt = (int) SrcY;
SrcXec = 64 - (int) ((SrcX - (double) SrcXnt) * 64);
SrcYec = 64 - (int) ((SrcY - (double) SrcYnt) * 64);
// Get the values of the four pixels up down right left
pSrcPtr1 = pSrcScan0 + (SrcXnt * SrcBitmapDataStride) + SrcYnt;
pSrcPtr2 = pSrcPtr1 + 1;
pSrcPtr3 = pSrcScan0 + ((SrcXnt+1) * SrcBitmapDataStride) + SrcYnt;
pSrcPtr4 = pSrcPtr3 + 1;
SrcYnvDec = (64-SrcYec);
(*pNewPtr) = (unsigned char)(((SrcYec * (*pSrcPtr1) + SrcYnvDec * (*pSrcPtr2)) * SrcXec +
(SrcYec * (*pSrcPtr3) + SrcYnvDec * (*pSrcPtr4)) * (64 - SrcXec)) >> 12);
}
}
}

Answer 1

Two things that could help: multiprocessing and SIMD. With multiprocessing you could break up the output image into tiles and have each processor work on the next available tile. You can use SIMD instructions (like SSE, AVX, AltiVec, etc.) to calculate multiple things at the same time, such as doing the same matrix math to multiple coordinates at the same time. You can even combine the two - use multiple processors running SIMD instructions to do as much work as possible. You didn't mention what platform you're working on.