【图像处理】快速双线性插值

前言

图像缩放是最常用的图像操作，最近实现了一次双线性插值，适用于单通道8位灰度图像，可以同时处理缩小，放大。

加速手段有三个：1) 使用可分离方式，先后处理行和列,  2)构建缓冲区，以避免对某些行重复插值。3) 使用整形运算，避免浮点运算。

代码

typedef struct _Image{

    int w;

    int h;

    int c;

    int step;

    int type;

    void* data;

} Image;

 

static void _ieInterpImageBilinear8UC1_RowFilter(unsigned char* src, long* dst, int len, int* leftIdx, int* rightIdx, long* weight, int shift)

{

    int i, j;

    for(i = 0, j = 1; i < len - 2; i+=2, j+=2){

        dst[i] = ((1<<shift) - weight[i])*src[leftIdx[i]] + weight[i]*src[rightIdx[i]];

        dst[j] = ((1<<shift) - weight[j])*src[leftIdx[j]] + weight[j]*src[rightIdx[j]];

 

    }

    for( ; j < len; j++){

        dst[j] = ((1<<shift) - weight[j])*src[leftIdx[j]] + weight[j]*src[rightIdx[j]];

    }

}

 

static void _ieInterpImageBilinear8UC1(Image* src, Image* dst)

{

    int i, j;

    int sw, sh, sstep;

    int dw, dh, dstep;

    unsigned char *sdata, *ddata;

    float horScaleRatio, verScaleRatio;

    long *rowBuf1, *rowBuf2;

    long *upLinePtr, *downLinePtr, *tempPtr;

    long *horWeight;

    int *horLeftIdx, *horRightIdx;

    int preVerUpIdx, preVerDownIdx;

    int shift = 8;

 

    sw = src->w; sh = src->h; sstep = src->step; sdata = (unsigned char*)(src->data);

    dw = dst->w; dh = dst->h; dstep = dst->step; ddata = (unsigned char*)(dst->data);

 

    horScaleRatio = sw / (float)(dw);

    verScaleRatio = sh / (float)(dh);

 

    rowBuf1 = new long[dw];

    rowBuf2 = new long[dw];

    horWeight = new long[dw];

    horLeftIdx = new int[dw];

    horRightIdx = new int[dw];

 

    //col interpolation

    //计算目标图像像素横向的左右邻居序号，和权重。

    for(i = 0; i < dw; i++){

        float pos = (i + 0.5f) * horScaleRatio;

        horLeftIdx[i] = (int)(IET_MAX(pos - 0.5f, 0));

        horRightIdx[i] = (int)(IET_MIN(pos + 0.5f, sw-1));

        horWeight[i] = (long) (fabs(pos - 0.5f - horLeftIdx[i]) * (1<<shift));

    }

 

    preVerUpIdx = -1;

    preVerDownIdx = -1;

    upLinePtr = rowBuf1;

    downLinePtr = rowBuf2;

    for(j = 0; j < dh; j++){       

        float pos = (j + 0.5f) * verScaleRatio;

        int verUpIdx = (int)(IET_MAX(pos - 0.5f, 0));

        int verDownIdx = (int)(IET_MIN(pos + 0.5f, sh-1));

        long verWeight = (long) (fabs(pos - 0.5f - verUpIdx) * (1<<shift));

 

        if(verUpIdx == preVerUpIdx && verDownIdx == preVerDownIdx){

            ;//do nothing

        }

        else if(verUpIdx == preVerDownIdx){

            IET_SWAP(upLinePtr, downLinePtr, tempPtr);

            _ieInterpImageBilinear8UC1_RowFilter(sdata + sstep*verDownIdx, downLinePtr, dw, horLeftIdx, horRightIdx, horWeight, shift);

        }else{

            _ieInterpImageBilinear8UC1_RowFilter(sdata + sstep*verUpIdx,   upLinePtr, dw, horLeftIdx, horRightIdx, horWeight, shift);

            _ieInterpImageBilinear8UC1_RowFilter(sdata + sstep*verDownIdx, downLinePtr, dw, horLeftIdx, horRightIdx, horWeight, shift);

        }       

 

        for(i = 0; i < dw; i++){

            ddata[dstep*j + i] = (unsigned char) ( (((1<<shift) - verWeight)*upLinePtr[i] + verWeight*downLinePtr[i]) >> (2*shift) );

        }

        preVerUpIdx = verUpIdx;

        preVerDownIdx = verDownIdx;

    }

    delete []rowBuf1;

    delete []rowBuf2;

    delete []horWeight;

    delete []horLeftIdx;

    delete []horRightIdx;

}

速度提升

使用四次展开循环，速度提升。code如下：

static void _ieInterpImageBilinear8UC1_Ver3_RowFilter(unsigned char* src, long* dst, int len, int* leftIdx, int* rightIdx, long* weight, int shift)

{

    int i;

    for(i = 0; i < len - 4; i+=4){

        *dst++ = ((1<<shift) - weight[i])*src[leftIdx[i]] + weight[i]*src[rightIdx[i]];

        *dst++ = ((1<<shift) - weight[i+1])*src[leftIdx[i+1]] + weight[i+1]*src[rightIdx[i+1]];

        *dst++ = ((1<<shift) - weight[i+2])*src[leftIdx[i+2]] + weight[i+2]*src[rightIdx[i+2]];

        *dst++ = ((1<<shift) - weight[i+3])*src[leftIdx[i+3]] + weight[i+3]*src[rightIdx[i+3]];

 

    }

    for( ; i < len; ++i){

        *dst++ = ((1<<shift) - weight[i])*src[leftIdx[i]] + weight[i]*src[rightIdx[i]];

    }

}

 

static void _ieInterpImageBilinear8UC1_Ver3(Image* src, Image* dst)

{

    int i, j;

    int sw, sh, sstep;

    int dw, dh, dstep;

    unsigned char *sdata, *ddata;

    float horScaleRatio, verScaleRatio;

    long *rowBuf1, *rowBuf2;

    long *upLinePtr, *downLinePtr, *tempPtr;

    long *horWeight;

    int *horLeftIdx, *horRightIdx;

    int preVerUpIdx, preVerDownIdx;

    int shift = 8;

 

    sw = src->w; sh = src->h; sstep = src->step; sdata = (unsigned char*)(src->data);

    dw = dst->w; dh = dst->h; dstep = dst->step; ddata = (unsigned char*)(dst->data);

 

    horScaleRatio = sw / (float)(dw);

    verScaleRatio = sh / (float)(dh);

 

    rowBuf1 = new long[dw];

    rowBuf2 = new long[dw];

    horWeight = new long[dw];

    horLeftIdx = new int[dw];

    horRightIdx = new int[dw];

 

    //col interpolation

    //计算目标图像像素横向的左右邻居序号，和权重。

    for(i = 0; i < dw; i++){

        float pos = (i + 0.5f) * horScaleRatio;

        horLeftIdx[i] = (int)(IET_MAX(pos - 0.5f, 0));

        horRightIdx[i] = (int)(IET_MIN(pos + 0.5f, sw-1));

        horWeight[i] = (long) (fabs(pos - 0.5f - horLeftIdx[i]) * (1<<shift));

    }

 

    preVerUpIdx = -1;

    preVerDownIdx = -1;

    upLinePtr = rowBuf1;

    downLinePtr = rowBuf2;

    for(j = 0; j < dh; j++){       

        float pos = (j + 0.5f) * verScaleRatio;

        int verUpIdx = (int)(IET_MAX(pos - 0.5f, 0));

        int verDownIdx = (int)(IET_MIN(pos + 0.5f, sh-1));

        long verWeight = (long) (fabs(pos - 0.5f - verUpIdx) * (1<<shift));

 

        if(verUpIdx == preVerUpIdx && verDownIdx == preVerDownIdx){

            ;//do nothing

        }

        else if(verUpIdx == preVerDownIdx){

            IET_SWAP(upLinePtr, downLinePtr, tempPtr);

            _ieInterpImageBilinear8UC1_Ver3_RowFilter(sdata + sstep*verDownIdx, downLinePtr, dw, horLeftIdx, horRightIdx, horWeight, shift);

        }else{

            _ieInterpImageBilinear8UC1_Ver3_RowFilter(sdata + sstep*verUpIdx,   upLinePtr, dw, horLeftIdx, horRightIdx, horWeight, shift);

            _ieInterpImageBilinear8UC1_Ver3_RowFilter(sdata + sstep*verDownIdx, downLinePtr, dw, horLeftIdx, horRightIdx, horWeight, shift);

        }       

 

        unsigned char* _ptr = ddata + dstep*j;

        for(i = 0; i < dw-4; i+=4){

            *_ptr++ = (unsigned char) ( (((1<<shift) - verWeight)*upLinePtr[i] + verWeight*downLinePtr[i]) >> (2*shift) );

            *_ptr++ = (unsigned char) ( (((1<<shift) - verWeight)*upLinePtr[i+1] + verWeight*downLinePtr[i+1]) >> (2*shift) );

            *_ptr++ = (unsigned char) ( (((1<<shift) - verWeight)*upLinePtr[i+2] + verWeight*downLinePtr[i+2]) >> (2*shift) );

            *_ptr++ = (unsigned char) ( (((1<<shift) - verWeight)*upLinePtr[i+3] + verWeight*downLinePtr[i+3]) >> (2*shift) );

        }

        for(; i < dw; i++){

            *_ptr++ = (unsigned char) ( (((1<<shift) - verWeight)*upLinePtr[i] + verWeight*downLinePtr[i]) >> (2*shift) );

        }

        preVerUpIdx = verUpIdx;

        preVerDownIdx = verDownIdx;

    }

    delete []rowBuf1;

    delete []rowBuf2;

    delete []horWeight;

    delete []horLeftIdx;

    delete []horRightIdx;

}

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转载请注明出处：http://blog.csdn.net/songzitea/article/details/11096361

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