I've been attempting to code a Bi-Cubic resize algorithm for in-memory bitmaps. I'm familiar with how bi-cubic interpolation works, and I've used both the Wikipedia article and existing implementations as a guide towards coding my own version.
The following is my simple implementation. Here, bmap is a vector containing the bitmap data, and get_subpixel is simply a function that treats the bitmap as a 3D array composed of X x Y x Channel pixels, and returns a single sub-pixel at the specified coordinates.
std::vector<unsigned char> bicubic_resize(
std::vector<unsigned char>& bmap, std::size_t bmap_width, std::size_t bmap_height,
std::size_t channels, std::size_t dest_width, std::size_t dest_height)
{
std::vector<unsigned char> out(dest_width * dest_height * 3);
const double tx = double(bmap_width) / dest_width;
const double ty = double(bmap_height) / dest_height;
const std::size_t row_stride = dest_width * channels;
unsigned char C[5] = { 0 };
for (unsigned i = 0; i < dest_height; ++i)
{
for (unsigned j = 0; j < dest_width; ++j)
{
const int x = int(tx * j);
const int y = int(ty * i);
const double dx = tx * j - x;
const double dy = ty * i - y;
for (int k = 0; k < 3; ++k)
{
for (int jj = 0; jj < 4; ++jj)
{
const int idx = y - 1 + jj;
unsigned char a0 = get_subpixel(bmap, idx, x, k);
unsigned char d0 = get_subpixel(bmap, idx, x - 1, k) - a0;
unsigned char d2 = get_subpixel(bmap, idx, x + 1, k) - a0;
unsigned char d3 = get_subpixel(bmap, idx, x + 2, k) - a0;
unsigned char a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
unsigned char a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
unsigned char a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
d0 = C[0] - C[1];
d2 = C[2] - C[1];
d3 = C[3] - C[1];
a0 = C[1];
a1 = -1.0 / 3 * d0 + d2 -1.0 / 6 * d3;
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
out[i * row_stride + j * channels + k] = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
}
}
}
}
return out;
}
This code works perfectly for certain destination sizes. For example, if the original bitmap is 500 X 366, and the destination size is 250 x 183, the algorithm works perfectly:
Original:
Resized:
However, for certain other destination sizes, such as 100 x 73, the destination image is distorted:
I've been going over the interpolation code, and I can't see what I'm doing incorrectly.
I'd appreciate any hints, suggestions, or answers.
Aside from mixing floating point and integer arithmetic, I suspect you're getting numeric overflow / underflow with some of your intermediate values.
One easy fix is to just be consistent and use floating point throughout. Right now you have:
unsigned char C[5] = { 0 };
for (unsigned i = 0; i < dest_height; ++i)
{
for (unsigned j = 0; j < dest_width; ++j)
{
const int x = int(tx * j);
const int y = int(ty * i);
const double dx = tx * j - x;
const double dy = ty * i - y;
for (int k = 0; k < 3; ++k)
{
for (int jj = 0; jj < 4; ++jj)
{
const int idx = y - 1 + jj;
unsigned char a0 = get_subpixel(bmap, idx, x, k);
unsigned char d0 = get_subpixel(bmap, idx, x - 1, k) - a0;
unsigned char d2 = get_subpixel(bmap, idx, x + 1, k) - a0;
unsigned char d3 = get_subpixel(bmap, idx, x + 2, k) - a0;
unsigned char a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
unsigned char a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
unsigned char a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
d0 = C[0] - C[1];
d2 = C[2] - C[1];
d3 = C[3] - C[1];
a0 = C[1];
a1 = -1.0 / 3 * d0 + d2 -1.0 / 6 * d3;
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
out[i * row_stride + j * channels + k] = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
}
}
}
}
You have a mixture of unsigned char, int and double. Each one of those 1.0 / 3 converts your 8-bit data to double-precision float, and then the assignment truncates it back.
Instead, why not just use float throughout?
float C[5] = { 0 };
for (unsigned i = 0; i < dest_height; ++i)
{
for (unsigned j = 0; j < dest_width; ++j)
{
const float x = float(tx * j);
const float y = float(ty * i);
const float dx = tx * j - x;
const float dy = ty * i - y;
for (int k = 0; k < 3; ++k)
{
for (int jj = 0; jj < 4; ++jj)
{
const int idx = y - 1 + jj;
float a0 = get_subpixel(bmap, idx, x, k);
float d0 = get_subpixel(bmap, idx, x - 1, k) - a0;
float d2 = get_subpixel(bmap, idx, x + 1, k) - a0;
float d3 = get_subpixel(bmap, idx, x + 2, k) - a0;
float a1 = -(1.0f / 3.0f) * d0 + d2 - (1.0f / 6.0f) * d3;
float a2 = 0.5f * d0 + 0.5f * d2;
float a3 = -(1.0f / 6.0f) * d0 - 0.5f * d2 + (1.0f / 6.0f) * d3;
C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
d0 = C[0] - C[1];
d2 = C[2] - C[1];
d3 = C[3] - C[1];
a0 = C[1];
a1 = -(1.0f / 3.0f) * d0 + d2 -(1.0f / 6.0f) * d3;
a2 = 0.5f * d0 + 0.5f * d2;
a3 = -(1.0f / 6.0f) * d0 - 0.5f * d2 + (1.0f / 6.0f) * d3;
out[i * row_stride + j * channels + k] = saturate( a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy );
}
}
}
}
Then define a function saturate that does this:
inline unsigned char saturate( float x )
{
return x > 255.0f ? 255
: x < 0.0f ? 0
: unsigned char(x);
}
That will fix your overflow issues, and give you better precision and likely better performance too.
If you need to further improve the performance, then you should look into fixed point arithmetic. But for now, I think the above implementation is better.
Also, one other thought: You should be able to get some further efficiencies by precomputing dx * dx, dx * dx * dx, and so on:
float C[5] = { 0 };
for (unsigned i = 0; i < dest_height; ++i)
{
for (unsigned j = 0; j < dest_width; ++j)
{
const float x = float(tx * j);
const float y = float(ty * i);
const float dx = tx * j - x, dx2 = dx * dx, dx3 = dx2 * dx;
const float dy = ty * i - y, dy2 = dy * dy, dy3 = dy2 * dy;
for (int k = 0; k < 3; ++k)
{
for (int jj = 0; jj < 4; ++jj)
{
const int idx = y - 1 + jj;
float a0 = get_subpixel(bmap, idx, x, k);
float d0 = get_subpixel(bmap, idx, x - 1, k) - a0;
float d2 = get_subpixel(bmap, idx, x + 1, k) - a0;
float d3 = get_subpixel(bmap, idx, x + 2, k) - a0;
float a1 = -(1.0f / 3.0f) * d0 + d2 - (1.0f / 6.0f) * d3;
float a2 = 0.5f * d0 + 0.5f * d2;
float a3 = -(1.0f / 6.0f) * d0 - 0.5f * d2 + (1.0f / 6.0f) * d3;
C[jj] = a0 + a1 * dx + a2 * dx2 + a3 * dx3;
d0 = C[0] - C[1];
d2 = C[2] - C[1];
d3 = C[3] - C[1];
a0 = C[1];
a1 = -(1.0f / 3.0f) * d0 + d2 -(1.0f / 6.0f) * d3;
a2 = 0.5f * d0 + 0.5f * d2;
a3 = -(1.0f / 6.0f) * d0 - 0.5f * d2 + (1.0f / 6.0f) * d3;
out[i * row_stride + j * channels + k] = saturate( a0 + a1 * dy + a2 * dy2 + a3 * dy3 );
}
}
}
}
来源：https://stackoverflow.com/questions/17640173/implementation-of-bi-cubic-resize