// Three-lobed Lanczos, the most common choice.
+// Note that if you change this, the accuracy for LANCZOS_TABLE_SIZE
+// needs to be recomputed.
#define LANCZOS_RADIUS 3.0
#include <epoxy/gl.h>
}
}
-float lanczos_weight(float x, float a)
+float lanczos_weight(float x)
{
- if (fabs(x) > a) {
+ if (fabs(x) > LANCZOS_RADIUS) {
return 0.0f;
} else {
- return sinc(M_PI * x) * sinc(M_PI * x / a);
+ return sinc(M_PI * x) * sinc((M_PI / LANCZOS_RADIUS) * x);
}
}
+// The weight function can be expensive to compute over and over again
+// (which will happen during e.g. a zoom), but it is also easy to interpolate
+// linearly. We compute the right half of the function (in the range of
+// 0..LANCZOS_RADIUS), with two guard elements for easier interpolation, and
+// linearly interpolate to get our function.
+//
+// We want to scale the table so that the maximum error is always smaller
+// than 1e-6. As per http://www-solar.mcs.st-andrews.ac.uk/~clare/Lectures/num-analysis/Numan_chap3.pdf,
+// the error for interpolating a function linearly between points [a,b] is
+//
+// e = 1/2 (x-a)(x-b) f''(u_x)
+//
+// for some point u_x in [a,b] (where f(x) is our Lanczos function; we're
+// assuming LANCZOS_RADIUS=3 from here on). Obviously this is bounded by
+// f''(x) over the entire range. Numeric optimization shows the maximum of
+// |f''(x)| to be in x=1.09369819474562880, with the value 2.40067758733152381.
+// So if the steps between consecutive values are called d, we get
+//
+// |e| <= 1/2 (d/2)^2 2.4007
+// |e| <= 0.1367 d^2
+//
+// Solve for e = 1e-6 yields a step size of 0.0027, which to cover the range
+// 0..3 needs 1109 steps. We round up to the next power of two, just to be sure.
+#define LANCZOS_TABLE_SIZE 2048
+bool lanczos_table_init_done = false;
+float lanczos_table[LANCZOS_TABLE_SIZE + 2];
+
+void init_lanczos_table()
+{
+ for (unsigned i = 0; i < LANCZOS_TABLE_SIZE + 2; ++i) {
+ lanczos_table[i] = lanczos_weight(float(i) * (LANCZOS_RADIUS / LANCZOS_TABLE_SIZE));
+ }
+ lanczos_table_init_done = true;
+}
+
+float lanczos_weight_cached(float x)
+{
+ if (!lanczos_table_init_done) {
+ // Could in theory race between two threads if we are unlucky,
+ // but that is harmless, since they'll write the same data.
+ init_lanczos_table();
+ }
+ x = fabs(x);
+ if (x > LANCZOS_RADIUS) {
+ return 0.0f;
+ }
+ float table_pos = x * (LANCZOS_TABLE_SIZE / LANCZOS_RADIUS);
+ int table_pos_int = int(table_pos); // Truncate towards zero.
+ float table_pos_frac = table_pos - table_pos_int;
+ assert(table_pos < LANCZOS_TABLE_SIZE + 2);
+ return lanczos_table[table_pos_int] +
+ table_pos_frac * (lanczos_table[table_pos_int + 1] - lanczos_table[table_pos_int]);
+}
+
// Euclid's algorithm, from Wikipedia.
unsigned gcd(unsigned a, unsigned b)
{
float pos2 = src[i + 1].pos;
assert(pos2 > pos1);
- fp16_int_t pos, total_weight;
+ DestFloat pos, total_weight;
float sum_sq_error;
combine_two_samples(w1, w2, pos1, pos2, num_subtexels, inv_num_subtexels, &pos, &total_weight, &sum_sq_error);
for (unsigned i = 0; i < num; ++i) {
sum += to_fp64(vals[i].weight);
}
+ double inv_sum = 1.0 / sum;
for (unsigned i = 0; i < num; ++i) {
- vals[i].weight = from_fp64<T>(to_fp64(vals[i].weight) / sum);
+ vals[i].weight = from_fp64<T>(to_fp64(vals[i].weight) * inv_sum);
}
}
}
{
float num_subtexels = src_size / movit_texel_subpixel_precision;
float inv_num_subtexels = movit_texel_subpixel_precision / src_size;
- int src_bilinear_samples = 0;
- for (unsigned y = 0; y < dst_samples; ++y) {
- unsigned num_samples_saved = combine_samples<DestFloat>(weights + y * src_samples, NULL, num_subtexels, inv_num_subtexels, src_samples, UINT_MAX);
- src_bilinear_samples = max<int>(src_bilinear_samples, src_samples - num_samples_saved);
+ unsigned max_samples_saved = UINT_MAX;
+ for (unsigned y = 0; y < dst_samples && max_samples_saved > 0; ++y) {
+ unsigned num_samples_saved = combine_samples<DestFloat>(weights + y * src_samples, NULL, num_subtexels, inv_num_subtexels, src_samples, max_samples_saved);
+ max_samples_saved = min(max_samples_saved, num_samples_saved);
}
// Now that we know the right width, actually combine the samples.
+ unsigned src_bilinear_samples = src_samples - max_samples_saved;
*bilinear_weights = new Tap<DestFloat>[dst_samples * src_bilinear_samples];
for (unsigned y = 0; y < dst_samples; ++y) {
Tap<DestFloat> *bilinear_weights_ptr = *bilinear_weights + y * src_bilinear_samples;
num_subtexels,
inv_num_subtexels,
src_samples,
- src_samples - src_bilinear_samples);
- assert(int(src_samples) - int(num_samples_saved) == src_bilinear_samples);
+ max_samples_saved);
+ assert(num_samples_saved == max_samples_saved);
normalize_sum(bilinear_weights_ptr, src_bilinear_samples);
}
return src_bilinear_samples;
// Now sample <int_radius> pixels on each side around that point.
for (int i = 0; i < src_samples; ++i) {
int src_y = base_src_y + i - int_radius;
- float weight = lanczos_weight(radius_scaling_factor * (src_y - center_src_y - subpixel_offset), LANCZOS_RADIUS);
+ float weight = lanczos_weight_cached(radius_scaling_factor * (src_y - center_src_y - subpixel_offset));
weights[y * src_samples + i].weight = weight * radius_scaling_factor;
weights[y * src_samples + i].pos = (src_y + 0.5) / float(src_size);
}