【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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