3d9155eb0a
Point Caches (used by particle system, cloth, boids etc.) are now always compressed, uzing zstd coupled with lossless data filtering. - This is both smaller cache files _and_ faster than the old "Heavy" compression mode, - And smaller data files and same or slightly faster speed than using no compression at all, - There was not much difference between compression levels once data filtering got added, so option to pick them was removed. - So there's no downside to just always using the compression, which makes for a simpler UI. - RNA change: removes PointCache.compression property. More details and cache size / performance numbers in the PR. Pull Request: https://projects.blender.org/blender/blender/pulls/144356
56 lines
1.6 KiB
C++
56 lines
1.6 KiB
C++
/* SPDX-FileCopyrightText: 2025 Blender Authors
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*
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* SPDX-License-Identifier: Apache-2.0 */
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#include "testing/testing.h"
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#include "BLI_array.hh"
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#include "BLI_compression.hh"
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namespace blender {
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TEST(compression, filter_transpose_delta)
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{
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constexpr int num = 5;
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constexpr int size = 3;
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uint8_t input[num * size] = {0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 5, 4, 3};
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uint8_t filtered_exp[num * size] = {0, 2, 6, 26, 227, 1, 2, 10, 42, 205, 1, 4, 16, 68, 170};
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uint8_t filtered[num * size] = {};
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uint8_t unfiltered[num * size] = {};
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filter_transpose_delta(input, filtered, num, size);
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EXPECT_EQ_ARRAY(filtered_exp, filtered, num * size);
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unfilter_transpose_delta(filtered, unfiltered, num, size);
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EXPECT_EQ_ARRAY(input, unfiltered, num * size);
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}
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static uint32_t pcg_rand(uint32_t &rng_state)
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{
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uint32_t state = rng_state;
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rng_state = rng_state * 747796405u + 2891336453u;
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uint32_t word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u;
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return (word >> 22u) ^ word;
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}
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TEST(compression, filter_transpose_delta_stress)
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{
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uint32_t rng = 1;
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constexpr int size = 64 * 1024;
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Array<uint8_t> input(size);
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Array<uint8_t> filtered(size);
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Array<uint8_t> unfiltered(size);
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for (uint8_t &val : input) {
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val = pcg_rand(rng);
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}
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const int strides[] = {1, 2, 3, 4, 5, 8, 13, 16, 25, 48, 64, 65, 101, 300, 512, 513, size};
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for (int stride : strides) {
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const int num = size / stride;
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filter_transpose_delta(input.data(), filtered.data(), num, stride);
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unfilter_transpose_delta(filtered.data(), unfiltered.data(), num, stride);
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EXPECT_EQ_ARRAY(input.data(), unfiltered.data(), num * stride);
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}
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}
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} // namespace blender
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