双线性插值算法ARM NEON优化

C语言版本双线性插值算法

inline double bilinear_interp(double x, double y, double v11, double v12,                              double v21, double v22) {    return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 * (1 - y) + v22 * y) * x;}

使用ARM NOEN优化后的双线性插值版本

inline uint8x8_t bilinear_interp_NEON(double x, double y, uint8x8_t v11,uint8x8_t v12,                                      uint8x8_t v21,uint8x8_t v22){    uint16x8_t v11_16 = vmovl_u8(v11);    uint16x8_t v12_16 = vmovl_u8(v12);    uint16x8_t v21_16 = vmovl_u8(v21);    uint16x8_t v22_16 = vmovl_u8(v22);    ///convert v11 to two float32x4    uint16x4_t v_16_low = vget_low_u16(v11_16);    uint16x4_t v_16_high = vget_high_u16(v11_16);    uint32x4_t v_32_low = vmovl_u16(v_16_low);    uint32x4_t v_32_high = vmovl_u16(v_16_high);    float32x4_t v11_32f_low = vcvtq_f32_u32(v_32_low);    float32x4_t v11_32f_high = vcvtq_f32_u32(v_32_high);    //v12    v_16_low = vget_low_u16(v12_16);    v_16_high = vget_high_u16(v12_16);    v_32_low = vmovl_u16(v_16_low);    v_32_high = vmovl_u16(v_16_high);    float32x4_t v12_32f_low = vcvtq_f32_u32(v_32_low);    float32x4_t v12_32f_high = vcvtq_f32_u32(v_32_high);    //v21    v_16_low = vget_low_u16(v21_16);    v_16_high = vget_high_u16(v21_16);    v_32_low = vmovl_u16(v_16_low);    v_32_high = vmovl_u16(v_16_high);    float32x4_t v21_32f_low = vcvtq_f32_u32(v_32_low);    float32x4_t v21_32f_high = vcvtq_f32_u32(v_32_high);    //v22    v_16_low = vget_low_u16(v22_16);    v_16_high = vget_high_u16(v22_16);    v_32_low = vmovl_u16(v_16_low);    v_32_high = vmovl_u16(v_16_high);    float32x4_t v22_32f_low = vcvtq_f32_u32(v_32_low);    float32x4_t v22_32f_high = vcvtq_f32_u32(v_32_high);    float32_t fx = (float32_t)x;    float32_t fy = (float32_t)y;    float32_t one_fx = 1-fx;    float32_t one_fy = 1-fy;    float32x4_t tmp1,tmp2,tmp3,tmp4,tmp5,tmp;    uint32x4_t result_32_low,result_32_high;    uint16x4_t result_16_low,result_16_high;    //for low  32x4    tmp1 = vmulq_n_f32(v11_32f_low, one_fy);    tmp2 = vmulq_n_f32(v12_32f_low, fy);    tmp3 = vaddq_f32(tmp1, tmp2);    tmp4 = vmulq_n_f32(tmp3, one_fx);    tmp1 = vmulq_n_f32(v21_32f_low, one_fy);    tmp2 = vmulq_n_f32(v22_32f_low, fy);    tmp3 = vaddq_f32(tmp1, tmp2);    tmp5 = vmulq_n_f32(tmp3, fx);    tmp = vaddq_f32(tmp4, tmp5);    result_32_low = vcvtq_u32_f32(tmp);    result_16_low = vqmovn_u32(result_32_low);    //for high 32x4    tmp1 = vmulq_n_f32(v11_32f_high, one_fy);    tmp2 = vmulq_n_f32(v12_32f_high, fy);    tmp3 = vaddq_f32(tmp1, tmp2);    tmp4 = vmulq_n_f32(tmp3, one_fx);    tmp1 = vmulq_n_f32(v21_32f_high, one_fy);    tmp2 = vmulq_n_f32(v22_32f_high, fy);    tmp3 = vaddq_f32(tmp1, tmp2);    tmp5 = vmulq_n_f32(tmp3, fx);    tmp = vaddq_f32(tmp4, tmp5);    result_32_high = vcvtq_u32_f32(tmp);    result_16_high = vqmovn_u32(result_32_high);    uint16x8_t result_16 = vcombine_u16(result_16_low,result_16_high);    uint8x8_t result_8 = vqmovn_u16(result_16);    return result_8; }

使用ARM NEON后，一次可以处理8个像素，成倍提高了运行的速度。实践中需要特别注意对边界的处理（行的开始和结尾处）。
到这里我们还不能满足。要追求更快！！！
注意上面的代码中虽然用了ARM NEON，但是在ARM 指令集的操作中用到了浮点操作。所以，还可以继续使用浮点数定点化的优化方式，优化后的代码如下：

inline uint8x8_t bilinear_interp_NEON_FixedPoint(double x, double y, uint8x8_t v11,uint8x8_t v12,                                      uint8x8_t v21,uint8x8_t v22){    uint16x8_t v11_16 = vmovl_u8(v11);    uint16x8_t v12_16 = vmovl_u8(v12);    uint16x8_t v21_16 = vmovl_u8(v21);    uint16x8_t v22_16 = vmovl_u8(v22);    uint16x4_t v_16_low = vget_low_u16(v11_16);    uint16x4_t v_16_high = vget_high_u16(v11_16);    uint32x4_t v11_32_low = vmovl_u16(v_16_low);    uint32x4_t v11_32_high = vmovl_u16(v_16_high);    v_16_low = vget_low_u16(v12_16);    v_16_high = vget_high_u16(v12_16);    uint32x4_t v12_32_low = vmovl_u16(v_16_low);    uint32x4_t v12_32_high = vmovl_u16(v_16_high);    v_16_low = vget_low_u16(v21_16);    v_16_high = vget_high_u16(v21_16);    uint32x4_t v21_32_low = vmovl_u16(v_16_low);    uint32x4_t v21_32_high = vmovl_u16(v_16_high);    v_16_low = vget_low_u16(v22_16);    v_16_high = vget_high_u16(v22_16);    uint32x4_t v22_32_low = vmovl_u16(v_16_low);    uint32x4_t v22_32_high = vmovl_u16(v_16_high);    unsigned int intX = x*4096;    unsigned int intY = y*4096;    unsigned int one_x = 4096-intX;    unsigned int one_y = 4096-intY;    uint32_t intX_32 = (uint32_t) intX;    uint32_t intY_32 = (uint32_t) intY;    uint32_t oneX_32 = (uint32_t) one_x;    uint32_t oneY_32 = (uint32_t) one_y;    uint32x4_t tmp1,tmp2,tmp3,tmp4,tmp5,tmp;    uint16x4_t result_16_low, result_16_high;    //for low 4 numbers    tmp1 = vmulq_n_u32(v11_32_low,oneY_32);    tmp2 = vmulq_n_u32(v12_32_low, intY_32);    tmp3 = vaddq_u32(tmp1, tmp2);    tmp4 = vmulq_n_u32(tmp3, oneX_32);    tmp1 = vmulq_n_u32(v21_32_low, oneY_32);    tmp2 = vmulq_n_u32(v22_32_low, intY_32);    tmp3 = vaddq_u32(tmp1, tmp2);    tmp5 = vmulq_n_u32(tmp3, intX_32);    tmp = vaddq_u32(tmp4, tmp5);    result_16_low = vshrn_n_u32(tmp,16); //shift right 16 bytes    result_16_low = vrshr_n_u16(result_16_low,8); //shift right 8 bytes, totally 24 bytes    //for high 4 numbers    tmp1 = vmulq_n_u32(v11_32_high,oneY_32);    tmp2 = vmulq_n_u32(v12_32_high, intY_32);    tmp3 = vaddq_u32(tmp1, tmp2);    tmp4 = vmulq_n_u32(tmp3, oneX_32);    tmp1 = vmulq_n_u32(v21_32_high, oneY_32);    tmp2 = vmulq_n_u32(v22_32_high, intY_32);    tmp3 = vaddq_u32(tmp1, tmp2);    tmp5 = vmulq_n_u32(tmp3, intX_32);    tmp = vaddq_u32(tmp4, tmp5);    result_16_high = vshrn_n_u32(tmp,16);  //shift right 16 bytes    result_16_high = vrshr_n_u16(result_16_high,8);  //shift right 8 bytes, totally 24 bytes    uint16x8_t result_16 = vcombine_u16(result_16_low,result_16_high);    uint8x8_t result_8 = vqmovn_u16(result_16);    return result_8;}

加入浮点定点化之后的优化，时间能进一步提升一倍左右。