| /* |
| * Copyright (c) 2023, Alliance for Open Media. All rights reserved |
| * |
| * This source code is subject to the terms of the BSD 2 Clause License and |
| * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License |
| * was not distributed with this source code in the LICENSE file, you can |
| * obtain it at www.aomedia.org/license/software. If the Alliance for Open |
| * Media Patent License 1.0 was not distributed with this source code in the |
| * PATENTS file, you can obtain it at www.aomedia.org/license/patent. |
| */ |
| |
| #include <arm_neon.h> |
| #include <assert.h> |
| #include <stdint.h> |
| |
| #include "aom_dsp/arm/mem_neon.h" |
| #include "aom_dsp/arm/sum_neon.h" |
| #include "av1/encoder/arm/pickrst_neon.h" |
| #include "av1/encoder/pickrst.h" |
| |
| static INLINE void highbd_calc_proj_params_r0_r1_neon( |
| const uint8_t *src8, int width, int height, int src_stride, |
| const uint8_t *dat8, int dat_stride, int32_t *flt0, int flt0_stride, |
| int32_t *flt1, int flt1_stride, int64_t H[2][2], int64_t C[2]) { |
| assert(width % 8 == 0); |
| const int size = width * height; |
| const uint16_t *src = CONVERT_TO_SHORTPTR(src8); |
| const uint16_t *dat = CONVERT_TO_SHORTPTR(dat8); |
| |
| int64x2_t h00_lo = vdupq_n_s64(0); |
| int64x2_t h00_hi = vdupq_n_s64(0); |
| int64x2_t h11_lo = vdupq_n_s64(0); |
| int64x2_t h11_hi = vdupq_n_s64(0); |
| int64x2_t h01_lo = vdupq_n_s64(0); |
| int64x2_t h01_hi = vdupq_n_s64(0); |
| int64x2_t c0_lo = vdupq_n_s64(0); |
| int64x2_t c0_hi = vdupq_n_s64(0); |
| int64x2_t c1_lo = vdupq_n_s64(0); |
| int64x2_t c1_hi = vdupq_n_s64(0); |
| |
| do { |
| const uint16_t *src_ptr = src; |
| const uint16_t *dat_ptr = dat; |
| int32_t *flt0_ptr = flt0; |
| int32_t *flt1_ptr = flt1; |
| int w = width; |
| |
| do { |
| uint16x8_t s = vld1q_u16(src_ptr); |
| uint16x8_t d = vld1q_u16(dat_ptr); |
| int32x4_t f0_lo = vld1q_s32(flt0_ptr); |
| int32x4_t f0_hi = vld1q_s32(flt0_ptr + 4); |
| int32x4_t f1_lo = vld1q_s32(flt1_ptr); |
| int32x4_t f1_hi = vld1q_s32(flt1_ptr + 4); |
| |
| int32x4_t u_lo = |
| vreinterpretq_s32_u32(vshll_n_u16(vget_low_u16(d), SGRPROJ_RST_BITS)); |
| int32x4_t u_hi = vreinterpretq_s32_u32( |
| vshll_n_u16(vget_high_u16(d), SGRPROJ_RST_BITS)); |
| int32x4_t s_lo = |
| vreinterpretq_s32_u32(vshll_n_u16(vget_low_u16(s), SGRPROJ_RST_BITS)); |
| int32x4_t s_hi = vreinterpretq_s32_u32( |
| vshll_n_u16(vget_high_u16(s), SGRPROJ_RST_BITS)); |
| s_lo = vsubq_s32(s_lo, u_lo); |
| s_hi = vsubq_s32(s_hi, u_hi); |
| |
| f0_lo = vsubq_s32(f0_lo, u_lo); |
| f0_hi = vsubq_s32(f0_hi, u_hi); |
| f1_lo = vsubq_s32(f1_lo, u_lo); |
| f1_hi = vsubq_s32(f1_hi, u_hi); |
| |
| h00_lo = vmlal_s32(h00_lo, vget_low_s32(f0_lo), vget_low_s32(f0_lo)); |
| h00_lo = vmlal_s32(h00_lo, vget_high_s32(f0_lo), vget_high_s32(f0_lo)); |
| h00_hi = vmlal_s32(h00_hi, vget_low_s32(f0_hi), vget_low_s32(f0_hi)); |
| h00_hi = vmlal_s32(h00_hi, vget_high_s32(f0_hi), vget_high_s32(f0_hi)); |
| |
| h11_lo = vmlal_s32(h11_lo, vget_low_s32(f1_lo), vget_low_s32(f1_lo)); |
| h11_lo = vmlal_s32(h11_lo, vget_high_s32(f1_lo), vget_high_s32(f1_lo)); |
| h11_hi = vmlal_s32(h11_hi, vget_low_s32(f1_hi), vget_low_s32(f1_hi)); |
| h11_hi = vmlal_s32(h11_hi, vget_high_s32(f1_hi), vget_high_s32(f1_hi)); |
| |
| h01_lo = vmlal_s32(h01_lo, vget_low_s32(f0_lo), vget_low_s32(f1_lo)); |
| h01_lo = vmlal_s32(h01_lo, vget_high_s32(f0_lo), vget_high_s32(f1_lo)); |
| h01_hi = vmlal_s32(h01_hi, vget_low_s32(f0_hi), vget_low_s32(f1_hi)); |
| h01_hi = vmlal_s32(h01_hi, vget_high_s32(f0_hi), vget_high_s32(f1_hi)); |
| |
| c0_lo = vmlal_s32(c0_lo, vget_low_s32(f0_lo), vget_low_s32(s_lo)); |
| c0_lo = vmlal_s32(c0_lo, vget_high_s32(f0_lo), vget_high_s32(s_lo)); |
| c0_hi = vmlal_s32(c0_hi, vget_low_s32(f0_hi), vget_low_s32(s_hi)); |
| c0_hi = vmlal_s32(c0_hi, vget_high_s32(f0_hi), vget_high_s32(s_hi)); |
| |
| c1_lo = vmlal_s32(c1_lo, vget_low_s32(f1_lo), vget_low_s32(s_lo)); |
| c1_lo = vmlal_s32(c1_lo, vget_high_s32(f1_lo), vget_high_s32(s_lo)); |
| c1_hi = vmlal_s32(c1_hi, vget_low_s32(f1_hi), vget_low_s32(s_hi)); |
| c1_hi = vmlal_s32(c1_hi, vget_high_s32(f1_hi), vget_high_s32(s_hi)); |
| |
| src_ptr += 8; |
| dat_ptr += 8; |
| flt0_ptr += 8; |
| flt1_ptr += 8; |
| w -= 8; |
| } while (w != 0); |
| |
| src += src_stride; |
| dat += dat_stride; |
| flt0 += flt0_stride; |
| flt1 += flt1_stride; |
| } while (--height != 0); |
| |
| H[0][0] = horizontal_add_s64x2(vaddq_s64(h00_lo, h00_hi)) / size; |
| H[0][1] = horizontal_add_s64x2(vaddq_s64(h01_lo, h01_hi)) / size; |
| H[1][1] = horizontal_add_s64x2(vaddq_s64(h11_lo, h11_hi)) / size; |
| H[1][0] = H[0][1]; |
| C[0] = horizontal_add_s64x2(vaddq_s64(c0_lo, c0_hi)) / size; |
| C[1] = horizontal_add_s64x2(vaddq_s64(c1_lo, c1_hi)) / size; |
| } |
| |
| static INLINE void highbd_calc_proj_params_r0_neon( |
| const uint8_t *src8, int width, int height, int src_stride, |
| const uint8_t *dat8, int dat_stride, int32_t *flt0, int flt0_stride, |
| int64_t H[2][2], int64_t C[2]) { |
| assert(width % 8 == 0); |
| const int size = width * height; |
| const uint16_t *src = CONVERT_TO_SHORTPTR(src8); |
| const uint16_t *dat = CONVERT_TO_SHORTPTR(dat8); |
| |
| int64x2_t h00_lo = vdupq_n_s64(0); |
| int64x2_t h00_hi = vdupq_n_s64(0); |
| int64x2_t c0_lo = vdupq_n_s64(0); |
| int64x2_t c0_hi = vdupq_n_s64(0); |
| |
| do { |
| const uint16_t *src_ptr = src; |
| const uint16_t *dat_ptr = dat; |
| int32_t *flt0_ptr = flt0; |
| int w = width; |
| |
| do { |
| uint16x8_t s = vld1q_u16(src_ptr); |
| uint16x8_t d = vld1q_u16(dat_ptr); |
| int32x4_t f0_lo = vld1q_s32(flt0_ptr); |
| int32x4_t f0_hi = vld1q_s32(flt0_ptr + 4); |
| |
| int32x4_t u_lo = |
| vreinterpretq_s32_u32(vshll_n_u16(vget_low_u16(d), SGRPROJ_RST_BITS)); |
| int32x4_t u_hi = vreinterpretq_s32_u32( |
| vshll_n_u16(vget_high_u16(d), SGRPROJ_RST_BITS)); |
| int32x4_t s_lo = |
| vreinterpretq_s32_u32(vshll_n_u16(vget_low_u16(s), SGRPROJ_RST_BITS)); |
| int32x4_t s_hi = vreinterpretq_s32_u32( |
| vshll_n_u16(vget_high_u16(s), SGRPROJ_RST_BITS)); |
| s_lo = vsubq_s32(s_lo, u_lo); |
| s_hi = vsubq_s32(s_hi, u_hi); |
| |
| f0_lo = vsubq_s32(f0_lo, u_lo); |
| f0_hi = vsubq_s32(f0_hi, u_hi); |
| |
| h00_lo = vmlal_s32(h00_lo, vget_low_s32(f0_lo), vget_low_s32(f0_lo)); |
| h00_lo = vmlal_s32(h00_lo, vget_high_s32(f0_lo), vget_high_s32(f0_lo)); |
| h00_hi = vmlal_s32(h00_hi, vget_low_s32(f0_hi), vget_low_s32(f0_hi)); |
| h00_hi = vmlal_s32(h00_hi, vget_high_s32(f0_hi), vget_high_s32(f0_hi)); |
| |
| c0_lo = vmlal_s32(c0_lo, vget_low_s32(f0_lo), vget_low_s32(s_lo)); |
| c0_lo = vmlal_s32(c0_lo, vget_high_s32(f0_lo), vget_high_s32(s_lo)); |
| c0_hi = vmlal_s32(c0_hi, vget_low_s32(f0_hi), vget_low_s32(s_hi)); |
| c0_hi = vmlal_s32(c0_hi, vget_high_s32(f0_hi), vget_high_s32(s_hi)); |
| |
| src_ptr += 8; |
| dat_ptr += 8; |
| flt0_ptr += 8; |
| w -= 8; |
| } while (w != 0); |
| |
| src += src_stride; |
| dat += dat_stride; |
| flt0 += flt0_stride; |
| } while (--height != 0); |
| |
| H[0][0] = horizontal_add_s64x2(vaddq_s64(h00_lo, h00_hi)) / size; |
| C[0] = horizontal_add_s64x2(vaddq_s64(c0_lo, c0_hi)) / size; |
| } |
| |
| static INLINE void highbd_calc_proj_params_r1_neon( |
| const uint8_t *src8, int width, int height, int src_stride, |
| const uint8_t *dat8, int dat_stride, int32_t *flt1, int flt1_stride, |
| int64_t H[2][2], int64_t C[2]) { |
| assert(width % 8 == 0); |
| const int size = width * height; |
| const uint16_t *src = CONVERT_TO_SHORTPTR(src8); |
| const uint16_t *dat = CONVERT_TO_SHORTPTR(dat8); |
| |
| int64x2_t h11_lo = vdupq_n_s64(0); |
| int64x2_t h11_hi = vdupq_n_s64(0); |
| int64x2_t c1_lo = vdupq_n_s64(0); |
| int64x2_t c1_hi = vdupq_n_s64(0); |
| |
| do { |
| const uint16_t *src_ptr = src; |
| const uint16_t *dat_ptr = dat; |
| int32_t *flt1_ptr = flt1; |
| int w = width; |
| |
| do { |
| uint16x8_t s = vld1q_u16(src_ptr); |
| uint16x8_t d = vld1q_u16(dat_ptr); |
| int32x4_t f1_lo = vld1q_s32(flt1_ptr); |
| int32x4_t f1_hi = vld1q_s32(flt1_ptr + 4); |
| |
| int32x4_t u_lo = |
| vreinterpretq_s32_u32(vshll_n_u16(vget_low_u16(d), SGRPROJ_RST_BITS)); |
| int32x4_t u_hi = vreinterpretq_s32_u32( |
| vshll_n_u16(vget_high_u16(d), SGRPROJ_RST_BITS)); |
| int32x4_t s_lo = |
| vreinterpretq_s32_u32(vshll_n_u16(vget_low_u16(s), SGRPROJ_RST_BITS)); |
| int32x4_t s_hi = vreinterpretq_s32_u32( |
| vshll_n_u16(vget_high_u16(s), SGRPROJ_RST_BITS)); |
| s_lo = vsubq_s32(s_lo, u_lo); |
| s_hi = vsubq_s32(s_hi, u_hi); |
| |
| f1_lo = vsubq_s32(f1_lo, u_lo); |
| f1_hi = vsubq_s32(f1_hi, u_hi); |
| |
| h11_lo = vmlal_s32(h11_lo, vget_low_s32(f1_lo), vget_low_s32(f1_lo)); |
| h11_lo = vmlal_s32(h11_lo, vget_high_s32(f1_lo), vget_high_s32(f1_lo)); |
| h11_hi = vmlal_s32(h11_hi, vget_low_s32(f1_hi), vget_low_s32(f1_hi)); |
| h11_hi = vmlal_s32(h11_hi, vget_high_s32(f1_hi), vget_high_s32(f1_hi)); |
| |
| c1_lo = vmlal_s32(c1_lo, vget_low_s32(f1_lo), vget_low_s32(s_lo)); |
| c1_lo = vmlal_s32(c1_lo, vget_high_s32(f1_lo), vget_high_s32(s_lo)); |
| c1_hi = vmlal_s32(c1_hi, vget_low_s32(f1_hi), vget_low_s32(s_hi)); |
| c1_hi = vmlal_s32(c1_hi, vget_high_s32(f1_hi), vget_high_s32(s_hi)); |
| |
| src_ptr += 8; |
| dat_ptr += 8; |
| flt1_ptr += 8; |
| w -= 8; |
| } while (w != 0); |
| |
| src += src_stride; |
| dat += dat_stride; |
| flt1 += flt1_stride; |
| } while (--height != 0); |
| |
| H[1][1] = horizontal_add_s64x2(vaddq_s64(h11_lo, h11_hi)) / size; |
| C[1] = horizontal_add_s64x2(vaddq_s64(c1_lo, c1_hi)) / size; |
| } |
| |
| // The function calls 3 subfunctions for the following cases : |
| // 1) When params->r[0] > 0 and params->r[1] > 0. In this case all elements |
| // of C and H need to be computed. |
| // 2) When only params->r[0] > 0. In this case only H[0][0] and C[0] are |
| // non-zero and need to be computed. |
| // 3) When only params->r[1] > 0. In this case only H[1][1] and C[1] are |
| // non-zero and need to be computed. |
| void av1_calc_proj_params_high_bd_neon(const uint8_t *src8, int width, |
| int height, int src_stride, |
| const uint8_t *dat8, int dat_stride, |
| int32_t *flt0, int flt0_stride, |
| int32_t *flt1, int flt1_stride, |
| int64_t H[2][2], int64_t C[2], |
| const sgr_params_type *params) { |
| if ((params->r[0] > 0) && (params->r[1] > 0)) { |
| highbd_calc_proj_params_r0_r1_neon(src8, width, height, src_stride, dat8, |
| dat_stride, flt0, flt0_stride, flt1, |
| flt1_stride, H, C); |
| } else if (params->r[0] > 0) { |
| highbd_calc_proj_params_r0_neon(src8, width, height, src_stride, dat8, |
| dat_stride, flt0, flt0_stride, H, C); |
| } else if (params->r[1] > 0) { |
| highbd_calc_proj_params_r1_neon(src8, width, height, src_stride, dat8, |
| dat_stride, flt1, flt1_stride, H, C); |
| } |
| } |
| |
| static INLINE int16x8_t tbl2q(int16x8_t a, int16x8_t b, uint8x16_t idx) { |
| #if AOM_ARCH_AARCH64 |
| uint8x16x2_t table = { { vreinterpretq_u8_s16(a), vreinterpretq_u8_s16(b) } }; |
| return vreinterpretq_s16_u8(vqtbl2q_u8(table, idx)); |
| #else |
| uint8x8x4_t table = { { vreinterpret_u8_s16(vget_low_s16(a)), |
| vreinterpret_u8_s16(vget_high_s16(a)), |
| vreinterpret_u8_s16(vget_low_s16(b)), |
| vreinterpret_u8_s16(vget_high_s16(b)) } }; |
| return vreinterpretq_s16_u8(vcombine_u8(vtbl4_u8(table, vget_low_u8(idx)), |
| vtbl4_u8(table, vget_high_u8(idx)))); |
| #endif |
| } |
| |
| static INLINE int16x8_t tbl3q(int16x8_t a, int16x8_t b, int16x8_t c, |
| uint8x16_t idx) { |
| #if AOM_ARCH_AARCH64 |
| uint8x16x3_t table = { { vreinterpretq_u8_s16(a), vreinterpretq_u8_s16(b), |
| vreinterpretq_u8_s16(c) } }; |
| return vreinterpretq_s16_u8(vqtbl3q_u8(table, idx)); |
| #else |
| // This is a specific implementation working only for compute stats with |
| // wiener_win == 5. |
| uint8x8x3_t table_lo = { { vreinterpret_u8_s16(vget_low_s16(a)), |
| vreinterpret_u8_s16(vget_high_s16(a)), |
| vreinterpret_u8_s16(vget_low_s16(b)) } }; |
| uint8x8x3_t table_hi = { { vreinterpret_u8_s16(vget_low_s16(b)), |
| vreinterpret_u8_s16(vget_high_s16(b)), |
| vreinterpret_u8_s16(vget_low_s16(c)) } }; |
| return vreinterpretq_s16_u8(vcombine_u8( |
| vtbl3_u8(table_lo, vget_low_u8(idx)), |
| vtbl3_u8(table_hi, vsub_u8(vget_high_u8(idx), vdup_n_u8(16))))); |
| #endif |
| } |
| |
| static INLINE int64_t div_shift_s64(int64_t x, int power) { |
| return (x < 0 ? x + (1ll << power) - 1 : x) >> power; |
| } |
| |
| // The M matrix is accumulated in a bitdepth-dependent number of steps to |
| // speed up the computation. This function computes the final M from the |
| // accumulated (src_s64) and the residual parts (src_s32). It also transposes |
| // the result as the output needs to be column-major. |
| static INLINE void acc_transpose_M(int64_t *dst, const int64_t *src_s64, |
| const int32_t *src_s32, const int wiener_win, |
| int shift) { |
| for (int i = 0; i < wiener_win; ++i) { |
| for (int j = 0; j < wiener_win; ++j) { |
| int tr_idx = j * wiener_win + i; |
| *dst++ = div_shift_s64(src_s64[tr_idx] + src_s32[tr_idx], shift); |
| } |
| } |
| } |
| |
| // The resulting H is a column-major matrix accumulated from the transposed |
| // (column-major) samples of the filter kernel (5x5 or 7x7) viewed as a single |
| // vector. For the 7x7 filter case: H(49x49) = [49 x 1] x [1 x 49]. This |
| // function transforms back to the originally expected format (double |
| // transpose). The H matrix is accumulated in a bitdepth-dependent number of |
| // steps to speed up the computation. This function computes the final H from |
| // the accumulated (src_s64) and the residual parts (src_s32). The computed H is |
| // only an upper triangle matrix, this function also fills the lower triangle of |
| // the resulting matrix. |
| static INLINE void update_H(int64_t *dst, const int64_t *src_s64, |
| const int32_t *src_s32, const int wiener_win, |
| int stride, int shift) { |
| // For a simplified theoretical 3x3 case where `wiener_win` is 3 and |
| // `wiener_win2` is 9, the M matrix is 3x3: |
| // 0, 3, 6 |
| // 1, 4, 7 |
| // 2, 5, 8 |
| // |
| // This is viewed as a vector to compute H (9x9) by vector outer product: |
| // 0, 3, 6, 1, 4, 7, 2, 5, 8 |
| // |
| // Double transpose and upper triangle remapping for 3x3 -> 9x9 case: |
| // 0, 3, 6, 1, 4, 7, 2, 5, 8, |
| // 3, 30, 33, 12, 31, 34, 21, 32, 35, |
| // 6, 33, 60, 15, 42, 61, 24, 51, 62, |
| // 1, 12, 15, 10, 13, 16, 11, 14, 17, |
| // 4, 31, 42, 13, 40, 43, 22, 41, 44, |
| // 7, 34, 61, 16, 43, 70, 25, 52, 71, |
| // 2, 21, 24, 11, 22, 25, 20, 23, 26, |
| // 5, 32, 51, 14, 41, 52, 23, 50, 53, |
| // 8, 35, 62, 17, 44, 71, 26, 53, 80, |
| const int wiener_win2 = wiener_win * wiener_win; |
| |
| // Loop through the indices according to the remapping above, along the |
| // columns: |
| // 0, wiener_win, 2 * wiener_win, ..., 1, 1 + 2 * wiener_win, ..., |
| // wiener_win - 1, wiener_win - 1 + wiener_win, ... |
| // For the 3x3 case `j` will be: 0, 3, 6, 1, 4, 7, 2, 5, 8. |
| for (int i = 0; i < wiener_win; ++i) { |
| for (int j = i; j < wiener_win2; j += wiener_win) { |
| // These two inner loops are the same as the two outer loops, but running |
| // along rows instead of columns. For the 3x3 case `l` will be: |
| // 0, 3, 6, 1, 4, 7, 2, 5, 8. |
| for (int k = 0; k < wiener_win; ++k) { |
| for (int l = k; l < wiener_win2; l += wiener_win) { |
| // The nominal double transpose indexing would be: |
| // int idx = stride * j + l; |
| // However we need the upper-right triangle, it is easy with some |
| // min/max operations. |
| int tr_idx = stride * AOMMIN(j, l) + AOMMAX(j, l); |
| |
| // Resulting matrix is filled by combining the 64-bit and the residual |
| // 32-bit matrices together with scaling. |
| *dst++ = div_shift_s64(src_s64[tr_idx] + src_s32[tr_idx], shift); |
| } |
| } |
| } |
| } |
| } |
| |
| // Load 7x7 matrix into 7 128-bit vectors from consecutive rows, the last load |
| // address is offset to prevent out-of-bounds access. |
| static INLINE void load_and_pack_s16_8x7(int16x8_t dst[7], const int16_t *src, |
| ptrdiff_t stride) { |
| dst[0] = vld1q_s16(src); |
| src += stride; |
| dst[1] = vld1q_s16(src); |
| src += stride; |
| dst[2] = vld1q_s16(src); |
| src += stride; |
| dst[3] = vld1q_s16(src); |
| src += stride; |
| dst[4] = vld1q_s16(src); |
| src += stride; |
| dst[5] = vld1q_s16(src); |
| src += stride; |
| dst[6] = vld1q_s16(src - 1); |
| } |
| |
| static INLINE void highbd_compute_stats_win7_neon( |
| const uint16_t *dgd, const uint16_t *src, int avg, int width, int height, |
| int dgd_stride, int src_stride, int64_t *M, int64_t *H, |
| aom_bit_depth_t bit_depth) { |
| // Matrix names are capitalized to help readability. |
| DECLARE_ALIGNED(64, int16_t, DGD_AVG0[WIENER_WIN2_ALIGN3]); |
| DECLARE_ALIGNED(64, int16_t, DGD_AVG1[WIENER_WIN2_ALIGN3]); |
| DECLARE_ALIGNED(64, int32_t, M_s32[WIENER_WIN2_ALIGN3]); |
| DECLARE_ALIGNED(64, int64_t, M_s64[WIENER_WIN2_ALIGN3]); |
| DECLARE_ALIGNED(64, int32_t, H_s32[WIENER_WIN2 * WIENER_WIN2_ALIGN2]); |
| DECLARE_ALIGNED(64, int64_t, H_s64[WIENER_WIN2 * WIENER_WIN2_ALIGN2]); |
| |
| memset(M_s32, 0, sizeof(M_s32)); |
| memset(M_s64, 0, sizeof(M_s64)); |
| memset(H_s32, 0, sizeof(H_s32)); |
| memset(H_s64, 0, sizeof(H_s64)); |
| |
| // Look-up tables to create 8x6 matrix with consecutive elements from two 7x7 |
| // matrices. |
| // clang-format off |
| DECLARE_ALIGNED(16, static const uint8_t, shuffle_stats7_highbd[192]) = { |
| 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, |
| 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, |
| 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, |
| 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, |
| 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, |
| 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, |
| 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, |
| 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, |
| 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, |
| 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, |
| 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, |
| 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, |
| }; |
| // clang-format on |
| |
| const uint8x16_t lut0 = vld1q_u8(shuffle_stats7_highbd + 0); |
| const uint8x16_t lut1 = vld1q_u8(shuffle_stats7_highbd + 16); |
| const uint8x16_t lut2 = vld1q_u8(shuffle_stats7_highbd + 32); |
| const uint8x16_t lut3 = vld1q_u8(shuffle_stats7_highbd + 48); |
| const uint8x16_t lut4 = vld1q_u8(shuffle_stats7_highbd + 64); |
| const uint8x16_t lut5 = vld1q_u8(shuffle_stats7_highbd + 80); |
| const uint8x16_t lut6 = vld1q_u8(shuffle_stats7_highbd + 96); |
| const uint8x16_t lut7 = vld1q_u8(shuffle_stats7_highbd + 112); |
| const uint8x16_t lut8 = vld1q_u8(shuffle_stats7_highbd + 128); |
| const uint8x16_t lut9 = vld1q_u8(shuffle_stats7_highbd + 144); |
| const uint8x16_t lut10 = vld1q_u8(shuffle_stats7_highbd + 160); |
| const uint8x16_t lut11 = vld1q_u8(shuffle_stats7_highbd + 176); |
| |
| // We can accumulate up to 65536/4096/256 8/10/12-bit multiplication results |
| // in 32-bit. We are processing 2 pixels at a time, so the accumulator max can |
| // be as high as 32768/2048/128 for the compute stats. |
| const int acc_cnt_max = (1 << (32 - 2 * bit_depth)) >> 1; |
| int acc_cnt = acc_cnt_max; |
| const int src_next = src_stride - width; |
| const int dgd_next = dgd_stride - width; |
| const int16x8_t avg_s16 = vdupq_n_s16(avg); |
| |
| do { |
| int j = width; |
| while (j >= 2) { |
| // Load two adjacent, overlapping 7x7 matrices: a 8x7 matrix with the |
| // middle 6x7 elements being shared. |
| int16x8_t dgd_rows[7]; |
| load_and_pack_s16_8x7(dgd_rows, (const int16_t *)dgd, dgd_stride); |
| |
| const int16_t *dgd_ptr = (const int16_t *)dgd + dgd_stride * 6; |
| dgd += 2; |
| |
| dgd_rows[0] = vsubq_s16(dgd_rows[0], avg_s16); |
| dgd_rows[1] = vsubq_s16(dgd_rows[1], avg_s16); |
| dgd_rows[2] = vsubq_s16(dgd_rows[2], avg_s16); |
| dgd_rows[3] = vsubq_s16(dgd_rows[3], avg_s16); |
| dgd_rows[4] = vsubq_s16(dgd_rows[4], avg_s16); |
| dgd_rows[5] = vsubq_s16(dgd_rows[5], avg_s16); |
| dgd_rows[6] = vsubq_s16(dgd_rows[6], avg_s16); |
| |
| // Re-arrange the combined 8x7 matrix to have the 2 whole 7x7 matrices (1 |
| // for each of the 2 pixels) separated into distinct int16x8_t[6] arrays. |
| // These arrays contain 48 elements of the 49 (7x7). Compute `dgd - avg` |
| // for both buffers. Each DGD_AVG buffer contains 49 consecutive elements. |
| int16x8_t dgd_avg0[6]; |
| int16x8_t dgd_avg1[6]; |
| |
| dgd_avg0[0] = tbl2q(dgd_rows[0], dgd_rows[1], lut0); |
| dgd_avg1[0] = tbl2q(dgd_rows[0], dgd_rows[1], lut6); |
| dgd_avg0[1] = tbl2q(dgd_rows[1], dgd_rows[2], lut1); |
| dgd_avg1[1] = tbl2q(dgd_rows[1], dgd_rows[2], lut7); |
| dgd_avg0[2] = tbl2q(dgd_rows[2], dgd_rows[3], lut2); |
| dgd_avg1[2] = tbl2q(dgd_rows[2], dgd_rows[3], lut8); |
| dgd_avg0[3] = tbl2q(dgd_rows[3], dgd_rows[4], lut3); |
| dgd_avg1[3] = tbl2q(dgd_rows[3], dgd_rows[4], lut9); |
| dgd_avg0[4] = tbl2q(dgd_rows[4], dgd_rows[5], lut4); |
| dgd_avg1[4] = tbl2q(dgd_rows[4], dgd_rows[5], lut10); |
| dgd_avg0[5] = tbl2q(dgd_rows[5], dgd_rows[6], lut5); |
| dgd_avg1[5] = tbl2q(dgd_rows[5], dgd_rows[6], lut11); |
| |
| vst1q_s16(DGD_AVG0, dgd_avg0[0]); |
| vst1q_s16(DGD_AVG1, dgd_avg1[0]); |
| vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); |
| vst1q_s16(DGD_AVG1 + 8, dgd_avg1[1]); |
| vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); |
| vst1q_s16(DGD_AVG1 + 16, dgd_avg1[2]); |
| vst1q_s16(DGD_AVG0 + 24, dgd_avg0[3]); |
| vst1q_s16(DGD_AVG1 + 24, dgd_avg1[3]); |
| vst1q_s16(DGD_AVG0 + 32, dgd_avg0[4]); |
| vst1q_s16(DGD_AVG1 + 32, dgd_avg1[4]); |
| vst1q_s16(DGD_AVG0 + 40, dgd_avg0[5]); |
| vst1q_s16(DGD_AVG1 + 40, dgd_avg1[5]); |
| |
| // The remaining last (49th) elements of `dgd - avg`. |
| DGD_AVG0[48] = dgd_ptr[6] - avg; |
| DGD_AVG1[48] = dgd_ptr[7] - avg; |
| |
| // Accumulate into row-major variant of matrix M (cross-correlation) for 2 |
| // output pixels at a time. M is of size 7 * 7. It needs to be filled such |
| // that multiplying one element from src with each element of a row of the |
| // wiener window will fill one column of M. However this is not very |
| // convenient in terms of memory access, as it means we do contiguous |
| // loads of dgd but strided stores to M. As a result, we use an |
| // intermediate matrix M_s32 which is instead filled such that one row of |
| // the wiener window gives one row of M_s32. Once fully computed, M_s32 is |
| // then transposed to return M. |
| int src_avg0 = *src++ - avg; |
| int src_avg1 = *src++ - avg; |
| int16x4_t src_avg0_s16 = vdup_n_s16(src_avg0); |
| int16x4_t src_avg1_s16 = vdup_n_s16(src_avg1); |
| update_M_2pixels(M_s32 + 0, src_avg0_s16, src_avg1_s16, dgd_avg0[0], |
| dgd_avg1[0]); |
| update_M_2pixels(M_s32 + 8, src_avg0_s16, src_avg1_s16, dgd_avg0[1], |
| dgd_avg1[1]); |
| update_M_2pixels(M_s32 + 16, src_avg0_s16, src_avg1_s16, dgd_avg0[2], |
| dgd_avg1[2]); |
| update_M_2pixels(M_s32 + 24, src_avg0_s16, src_avg1_s16, dgd_avg0[3], |
| dgd_avg1[3]); |
| update_M_2pixels(M_s32 + 32, src_avg0_s16, src_avg1_s16, dgd_avg0[4], |
| dgd_avg1[4]); |
| update_M_2pixels(M_s32 + 40, src_avg0_s16, src_avg1_s16, dgd_avg0[5], |
| dgd_avg1[5]); |
| |
| // Last (49th) element of M_s32 can be computed as scalar more efficiently |
| // for 2 output pixels. |
| M_s32[48] += DGD_AVG0[48] * src_avg0 + DGD_AVG1[48] * src_avg1; |
| |
| // Start accumulating into row-major version of matrix H |
| // (auto-covariance), it expects the DGD_AVG[01] matrices to also be |
| // row-major. H is of size 49 * 49. It is filled by multiplying every pair |
| // of elements of the wiener window together (vector outer product). Since |
| // it is a symmetric matrix, we only compute the upper-right triangle, and |
| // then copy it down to the lower-left later. The upper triangle is |
| // covered by 4x4 tiles. The original algorithm assumes the M matrix is |
| // column-major and the resulting H matrix is also expected to be |
| // column-major. It is not efficient to work with column-major matrices, |
| // so we accumulate into a row-major matrix H_s32. At the end of the |
| // algorithm a double transpose transformation will convert H_s32 back to |
| // the expected output layout. |
| update_H_7x7_2pixels(H_s32, DGD_AVG0, DGD_AVG1); |
| |
| // The last element of the triangle of H_s32 matrix can be computed as a |
| // scalar more efficiently. |
| H_s32[48 * WIENER_WIN2_ALIGN2 + 48] += |
| DGD_AVG0[48] * DGD_AVG0[48] + DGD_AVG1[48] * DGD_AVG1[48]; |
| |
| // Accumulate into 64-bit after a bit depth dependent number of iterations |
| // to prevent overflow. |
| if (--acc_cnt == 0) { |
| acc_cnt = acc_cnt_max; |
| |
| accumulate_and_clear(M_s64, M_s32, WIENER_WIN2_ALIGN2); |
| |
| // The widening accumulation is only needed for the upper triangle part |
| // of the matrix. |
| int64_t *lh = H_s64; |
| int32_t *lh32 = H_s32; |
| for (int k = 0; k < WIENER_WIN2; ++k) { |
| // The widening accumulation is only run for the relevant parts |
| // (upper-right triangle) in a row 4-element aligned. |
| int k4 = k / 4 * 4; |
| accumulate_and_clear(lh + k4, lh32 + k4, 48 - k4); |
| |
| // Last element of the row is computed separately. |
| lh[48] += lh32[48]; |
| lh32[48] = 0; |
| |
| lh += WIENER_WIN2_ALIGN2; |
| lh32 += WIENER_WIN2_ALIGN2; |
| } |
| } |
| |
| j -= 2; |
| } |
| |
| // Computations for odd pixel in the row. |
| if (width & 1) { |
| // Load two adjacent, overlapping 7x7 matrices: a 8x7 matrix with the |
| // middle 6x7 elements being shared. |
| int16x8_t dgd_rows[7]; |
| load_and_pack_s16_8x7(dgd_rows, (const int16_t *)dgd, dgd_stride); |
| |
| const int16_t *dgd_ptr = (const int16_t *)dgd + dgd_stride * 6; |
| ++dgd; |
| |
| // Re-arrange the combined 8x7 matrix to have a whole 7x7 matrix tightly |
| // packed into a int16x8_t[6] array. This array contains 48 elements of |
| // the 49 (7x7). Compute `dgd - avg` for the whole buffer. The DGD_AVG |
| // buffer contains 49 consecutive elements. |
| int16x8_t dgd_avg0[6]; |
| |
| dgd_avg0[0] = vsubq_s16(tbl2q(dgd_rows[0], dgd_rows[1], lut0), avg_s16); |
| dgd_avg0[1] = vsubq_s16(tbl2q(dgd_rows[1], dgd_rows[2], lut1), avg_s16); |
| dgd_avg0[2] = vsubq_s16(tbl2q(dgd_rows[2], dgd_rows[3], lut2), avg_s16); |
| dgd_avg0[3] = vsubq_s16(tbl2q(dgd_rows[3], dgd_rows[4], lut3), avg_s16); |
| dgd_avg0[4] = vsubq_s16(tbl2q(dgd_rows[4], dgd_rows[5], lut4), avg_s16); |
| dgd_avg0[5] = vsubq_s16(tbl2q(dgd_rows[5], dgd_rows[6], lut5), avg_s16); |
| |
| vst1q_s16(DGD_AVG0, dgd_avg0[0]); |
| vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); |
| vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); |
| vst1q_s16(DGD_AVG0 + 24, dgd_avg0[3]); |
| vst1q_s16(DGD_AVG0 + 32, dgd_avg0[4]); |
| vst1q_s16(DGD_AVG0 + 40, dgd_avg0[5]); |
| |
| // The remaining last (49th) element of `dgd - avg`. |
| DGD_AVG0[48] = dgd_ptr[6] - avg; |
| |
| // Accumulate into row-major order variant of matrix M (cross-correlation) |
| // for 1 output pixel at a time. M is of size 7 * 7. It needs to be filled |
| // such that multiplying one element from src with each element of a row |
| // of the wiener window will fill one column of M. However this is not |
| // very convenient in terms of memory access, as it means we do |
| // contiguous loads of dgd but strided stores to M. As a result, we use an |
| // intermediate matrix M_s32 which is instead filled such that one row of |
| // the wiener window gives one row of M_s32. Once fully computed, M_s32 is |
| // then transposed to return M. |
| int src_avg0 = *src++ - avg; |
| int16x4_t src_avg0_s16 = vdup_n_s16(src_avg0); |
| update_M_1pixel(M_s32 + 0, src_avg0_s16, dgd_avg0[0]); |
| update_M_1pixel(M_s32 + 8, src_avg0_s16, dgd_avg0[1]); |
| update_M_1pixel(M_s32 + 16, src_avg0_s16, dgd_avg0[2]); |
| update_M_1pixel(M_s32 + 24, src_avg0_s16, dgd_avg0[3]); |
| update_M_1pixel(M_s32 + 32, src_avg0_s16, dgd_avg0[4]); |
| update_M_1pixel(M_s32 + 40, src_avg0_s16, dgd_avg0[5]); |
| |
| // Last (49th) element of M_s32 can be computed as scalar more efficiently |
| // for 1 output pixel. |
| M_s32[48] += DGD_AVG0[48] * src_avg0; |
| |
| // Start accumulating into row-major order version of matrix H |
| // (auto-covariance), it expects the DGD_AVG0 matrix to also be row-major. |
| // H is of size 49 * 49. It is filled by multiplying every pair of |
| // elements of the wiener window together (vector outer product). Since it |
| // is a symmetric matrix, we only compute the upper-right triangle, and |
| // then copy it down to the lower-left later. The upper triangle is |
| // covered by 4x4 tiles. The original algorithm assumes the M matrix is |
| // column-major and the resulting H matrix is also expected to be |
| // column-major. It is not efficient to work column-major matrices, so we |
| // accumulate into a row-major matrix H_s32. At the end of the algorithm a |
| // double transpose transformation will convert H_s32 back to the expected |
| // output layout. |
| update_H_1pixel(H_s32, DGD_AVG0, WIENER_WIN2_ALIGN2, 48); |
| |
| // The last element of the triangle of H_s32 matrix can be computed as |
| // scalar more efficiently. |
| H_s32[48 * WIENER_WIN2_ALIGN2 + 48] += DGD_AVG0[48] * DGD_AVG0[48]; |
| } |
| |
| src += src_next; |
| dgd += dgd_next; |
| } while (--height != 0); |
| |
| int bit_depth_shift = bit_depth - AOM_BITS_8; |
| |
| acc_transpose_M(M, M_s64, M_s32, WIENER_WIN, bit_depth_shift); |
| |
| update_H(H, H_s64, H_s32, WIENER_WIN, WIENER_WIN2_ALIGN2, bit_depth_shift); |
| } |
| |
| // Load 5x5 matrix into 5 128-bit vectors from consecutive rows, the last load |
| // address is offset to prevent out-of-bounds access. |
| static INLINE void load_and_pack_s16_6x5(int16x8_t dst[5], const int16_t *src, |
| ptrdiff_t stride) { |
| dst[0] = vld1q_s16(src); |
| src += stride; |
| dst[1] = vld1q_s16(src); |
| src += stride; |
| dst[2] = vld1q_s16(src); |
| src += stride; |
| dst[3] = vld1q_s16(src); |
| src += stride; |
| dst[4] = vld1q_s16(src - 3); |
| } |
| |
| static void highbd_compute_stats_win5_neon(const uint16_t *dgd, |
| const uint16_t *src, int avg, |
| int width, int height, |
| int dgd_stride, int src_stride, |
| int64_t *M, int64_t *H, |
| aom_bit_depth_t bit_depth) { |
| // Matrix names are capitalized to help readability. |
| DECLARE_ALIGNED(64, int16_t, DGD_AVG0[WIENER_WIN2_REDUCED_ALIGN3]); |
| DECLARE_ALIGNED(64, int16_t, DGD_AVG1[WIENER_WIN2_REDUCED_ALIGN3]); |
| DECLARE_ALIGNED(64, int32_t, M_s32[WIENER_WIN2_REDUCED_ALIGN3]); |
| DECLARE_ALIGNED(64, int64_t, M_s64[WIENER_WIN2_REDUCED_ALIGN3]); |
| DECLARE_ALIGNED(64, int32_t, |
| H_s32[WIENER_WIN2_REDUCED * WIENER_WIN2_REDUCED_ALIGN2]); |
| DECLARE_ALIGNED(64, int64_t, |
| H_s64[WIENER_WIN2_REDUCED * WIENER_WIN2_REDUCED_ALIGN2]); |
| |
| memset(M_s32, 0, sizeof(M_s32)); |
| memset(M_s64, 0, sizeof(M_s64)); |
| memset(H_s32, 0, sizeof(H_s32)); |
| memset(H_s64, 0, sizeof(H_s64)); |
| |
| // Look-up tables to create 8x3 matrix with consecutive elements from 5x5 |
| // matrix. |
| DECLARE_ALIGNED(16, static const uint8_t, shuffle_stats5_highbd[96]) = { |
| 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 18, 19, 20, 21, |
| 6, 7, 8, 9, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 32, 33, |
| 2, 3, 4, 5, 6, 7, 8, 9, 22, 23, 24, 25, 26, 27, 28, 29, |
| 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 18, 19, 20, 21, 22, 23, |
| 8, 9, 10, 11, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 34, 35, |
| 4, 5, 6, 7, 8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, |
| }; |
| |
| const uint8x16_t lut0 = vld1q_u8(shuffle_stats5_highbd + 0); |
| const uint8x16_t lut1 = vld1q_u8(shuffle_stats5_highbd + 16); |
| const uint8x16_t lut2 = vld1q_u8(shuffle_stats5_highbd + 32); |
| const uint8x16_t lut3 = vld1q_u8(shuffle_stats5_highbd + 48); |
| const uint8x16_t lut4 = vld1q_u8(shuffle_stats5_highbd + 64); |
| const uint8x16_t lut5 = vld1q_u8(shuffle_stats5_highbd + 80); |
| |
| // We can accumulate up to 65536/4096/256 8/10/12-bit multiplication results |
| // in 32-bit. We are processing 2 pixels at a time, so the accumulator max can |
| // be as high as 32768/2048/128 for the compute stats. |
| const int acc_cnt_max = (1 << (32 - 2 * bit_depth)) >> 1; |
| int acc_cnt = acc_cnt_max; |
| const int src_next = src_stride - width; |
| const int dgd_next = dgd_stride - width; |
| const int16x8_t avg_s16 = vdupq_n_s16(avg); |
| |
| do { |
| int j = width; |
| while (j >= 2) { |
| // Load two adjacent, overlapping 5x5 matrices: a 6x5 matrix with the |
| // middle 4x5 elements being shared. |
| int16x8_t dgd_rows[5]; |
| load_and_pack_s16_6x5(dgd_rows, (const int16_t *)dgd, dgd_stride); |
| |
| const int16_t *dgd_ptr = (const int16_t *)dgd + dgd_stride * 4; |
| dgd += 2; |
| |
| dgd_rows[0] = vsubq_s16(dgd_rows[0], avg_s16); |
| dgd_rows[1] = vsubq_s16(dgd_rows[1], avg_s16); |
| dgd_rows[2] = vsubq_s16(dgd_rows[2], avg_s16); |
| dgd_rows[3] = vsubq_s16(dgd_rows[3], avg_s16); |
| dgd_rows[4] = vsubq_s16(dgd_rows[4], avg_s16); |
| |
| // Re-arrange the combined 6x5 matrix to have the 2 whole 5x5 matrices (1 |
| // for each of the 2 pixels) separated into distinct int16x8_t[3] arrays. |
| // These arrays contain 24 elements of the 25 (5x5). Compute `dgd - avg` |
| // for both buffers. Each DGD_AVG buffer contains 25 consecutive elements. |
| int16x8_t dgd_avg0[3]; |
| int16x8_t dgd_avg1[3]; |
| |
| dgd_avg0[0] = tbl2q(dgd_rows[0], dgd_rows[1], lut0); |
| dgd_avg1[0] = tbl2q(dgd_rows[0], dgd_rows[1], lut3); |
| dgd_avg0[1] = tbl3q(dgd_rows[1], dgd_rows[2], dgd_rows[3], lut1); |
| dgd_avg1[1] = tbl3q(dgd_rows[1], dgd_rows[2], dgd_rows[3], lut4); |
| dgd_avg0[2] = tbl2q(dgd_rows[3], dgd_rows[4], lut2); |
| dgd_avg1[2] = tbl2q(dgd_rows[3], dgd_rows[4], lut5); |
| |
| vst1q_s16(DGD_AVG0, dgd_avg0[0]); |
| vst1q_s16(DGD_AVG1, dgd_avg1[0]); |
| vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); |
| vst1q_s16(DGD_AVG1 + 8, dgd_avg1[1]); |
| vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); |
| vst1q_s16(DGD_AVG1 + 16, dgd_avg1[2]); |
| |
| // The remaining last (25th) elements of `dgd - avg`. |
| DGD_AVG0[24] = dgd_ptr[4] - avg; |
| DGD_AVG1[24] = dgd_ptr[5] - avg; |
| |
| // Accumulate into row-major variant of matrix M (cross-correlation) for 2 |
| // output pixels at a time. M is of size 5 * 5. It needs to be filled such |
| // that multiplying one element from src with each element of a row of the |
| // wiener window will fill one column of M. However this is not very |
| // convenient in terms of memory access, as it means we do contiguous |
| // loads of dgd but strided stores to M. As a result, we use an |
| // intermediate matrix M_s32 which is instead filled such that one row of |
| // the wiener window gives one row of M_s32. Once fully computed, M_s32 is |
| // then transposed to return M. |
| int src_avg0 = *src++ - avg; |
| int src_avg1 = *src++ - avg; |
| int16x4_t src_avg0_s16 = vdup_n_s16(src_avg0); |
| int16x4_t src_avg1_s16 = vdup_n_s16(src_avg1); |
| update_M_2pixels(M_s32 + 0, src_avg0_s16, src_avg1_s16, dgd_avg0[0], |
| dgd_avg1[0]); |
| update_M_2pixels(M_s32 + 8, src_avg0_s16, src_avg1_s16, dgd_avg0[1], |
| dgd_avg1[1]); |
| update_M_2pixels(M_s32 + 16, src_avg0_s16, src_avg1_s16, dgd_avg0[2], |
| dgd_avg1[2]); |
| |
| // Last (25th) element of M_s32 can be computed as scalar more efficiently |
| // for 2 output pixels. |
| M_s32[24] += DGD_AVG0[24] * src_avg0 + DGD_AVG1[24] * src_avg1; |
| |
| // Start accumulating into row-major version of matrix H |
| // (auto-covariance), it expects the DGD_AVG[01] matrices to also be |
| // row-major. H is of size 25 * 25. It is filled by multiplying every pair |
| // of elements of the wiener window together (vector outer product). Since |
| // it is a symmetric matrix, we only compute the upper-right triangle, and |
| // then copy it down to the lower-left later. The upper triangle is |
| // covered by 4x4 tiles. The original algorithm assumes the M matrix is |
| // column-major and the resulting H matrix is also expected to be |
| // column-major. It is not efficient to work with column-major matrices, |
| // so we accumulate into a row-major matrix H_s32. At the end of the |
| // algorithm a double transpose transformation will convert H_s32 back to |
| // the expected output layout. |
| update_H_5x5_2pixels(H_s32, DGD_AVG0, DGD_AVG1); |
| |
| // The last element of the triangle of H_s32 matrix can be computed as a |
| // scalar more efficiently. |
| H_s32[24 * WIENER_WIN2_REDUCED_ALIGN2 + 24] += |
| DGD_AVG0[24] * DGD_AVG0[24] + DGD_AVG1[24] * DGD_AVG1[24]; |
| |
| // Accumulate into 64-bit after a bit depth dependent number of iterations |
| // to prevent overflow. |
| if (--acc_cnt == 0) { |
| acc_cnt = acc_cnt_max; |
| |
| accumulate_and_clear(M_s64, M_s32, WIENER_WIN2_REDUCED_ALIGN2); |
| |
| // The widening accumulation is only needed for the upper triangle part |
| // of the matrix. |
| int64_t *lh = H_s64; |
| int32_t *lh32 = H_s32; |
| for (int k = 0; k < WIENER_WIN2_REDUCED; ++k) { |
| // The widening accumulation is only run for the relevant parts |
| // (upper-right triangle) in a row 4-element aligned. |
| int k4 = k / 4 * 4; |
| accumulate_and_clear(lh + k4, lh32 + k4, 24 - k4); |
| |
| // Last element of the row is computed separately. |
| lh[24] += lh32[24]; |
| lh32[24] = 0; |
| |
| lh += WIENER_WIN2_REDUCED_ALIGN2; |
| lh32 += WIENER_WIN2_REDUCED_ALIGN2; |
| } |
| } |
| |
| j -= 2; |
| } |
| |
| // Computations for odd pixel in the row. |
| if (width & 1) { |
| // Load two adjacent, overlapping 5x5 matrices: a 6x5 matrix with the |
| // middle 4x5 elements being shared. |
| int16x8_t dgd_rows[5]; |
| load_and_pack_s16_6x5(dgd_rows, (const int16_t *)dgd, dgd_stride); |
| |
| const int16_t *dgd_ptr = (const int16_t *)dgd + dgd_stride * 4; |
| ++dgd; |
| |
| // Re-arrange (and widen) the combined 6x5 matrix to have a whole 5x5 |
| // matrix tightly packed into a int16x8_t[3] array. This array contains |
| // 24 elements of the 25 (5x5). Compute `dgd - avg` for the whole buffer. |
| // The DGD_AVG buffer contains 25 consecutive elements. |
| int16x8_t dgd_avg0[3]; |
| |
| dgd_avg0[0] = vsubq_s16(tbl2q(dgd_rows[0], dgd_rows[1], lut0), avg_s16); |
| dgd_avg0[1] = vsubq_s16( |
| tbl3q(dgd_rows[1], dgd_rows[2], dgd_rows[3], lut1), avg_s16); |
| dgd_avg0[2] = vsubq_s16(tbl2q(dgd_rows[3], dgd_rows[4], lut2), avg_s16); |
| |
| vst1q_s16(DGD_AVG0, dgd_avg0[0]); |
| vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); |
| vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); |
| |
| // The remaining last (25th) element of `dgd - avg`. |
| DGD_AVG0[24] = dgd_ptr[4] - avg; |
| DGD_AVG1[24] = dgd_ptr[5] - avg; |
| |
| // Accumulate into row-major order variant of matrix M (cross-correlation) |
| // for 1 output pixel at a time. M is of size 5 * 5. It needs to be filled |
| // such that multiplying one element from src with each element of a row |
| // of the wiener window will fill one column of M. However this is not |
| // very convenient in terms of memory access, as it means we do |
| // contiguous loads of dgd but strided stores to M. As a result, we use an |
| // intermediate matrix M_s32 which is instead filled such that one row of |
| // the wiener window gives one row of M_s32. Once fully computed, M_s32 is |
| // then transposed to return M. |
| int src_avg0 = *src++ - avg; |
| int16x4_t src_avg0_s16 = vdup_n_s16(src_avg0); |
| update_M_1pixel(M_s32 + 0, src_avg0_s16, dgd_avg0[0]); |
| update_M_1pixel(M_s32 + 8, src_avg0_s16, dgd_avg0[1]); |
| update_M_1pixel(M_s32 + 16, src_avg0_s16, dgd_avg0[2]); |
| |
| // Last (25th) element of M_s32 can be computed as scalar more efficiently |
| // for 1 output pixel. |
| M_s32[24] += DGD_AVG0[24] * src_avg0; |
| |
| // Start accumulating into row-major order version of matrix H |
| // (auto-covariance), it expects the DGD_AVG0 matrix to also be row-major. |
| // H is of size 25 * 25. It is filled by multiplying every pair of |
| // elements of the wiener window together (vector outer product). Since it |
| // is a symmetric matrix, we only compute the upper-right triangle, and |
| // then copy it down to the lower-left later. The upper triangle is |
| // covered by 4x4 tiles. The original algorithm assumes the M matrix is |
| // column-major and the resulting H matrix is also expected to be |
| // column-major. It is not efficient to work with column-major matrices, |
| // so we accumulate into a row-major matrix H_s32. At the end of the |
| // algorithm a double transpose transformation will convert H_s32 back to |
| // the expected output layout. |
| update_H_1pixel(H_s32, DGD_AVG0, WIENER_WIN2_REDUCED_ALIGN2, 24); |
| |
| // The last element of the triangle of H_s32 matrix can be computed as a |
| // scalar more efficiently. |
| H_s32[24 * WIENER_WIN2_REDUCED_ALIGN2 + 24] += |
| DGD_AVG0[24] * DGD_AVG0[24]; |
| } |
| |
| src += src_next; |
| dgd += dgd_next; |
| } while (--height != 0); |
| |
| int bit_depth_shift = bit_depth - AOM_BITS_8; |
| |
| acc_transpose_M(M, M_s64, M_s32, WIENER_WIN_REDUCED, bit_depth_shift); |
| |
| update_H(H, H_s64, H_s32, WIENER_WIN_REDUCED, WIENER_WIN2_REDUCED_ALIGN2, |
| bit_depth_shift); |
| } |
| |
| static uint16_t highbd_find_average_neon(const uint16_t *src, int src_stride, |
| int width, int height) { |
| assert(width > 0); |
| assert(height > 0); |
| |
| uint64x2_t sum_u64 = vdupq_n_u64(0); |
| uint64_t sum = 0; |
| |
| int h = height; |
| do { |
| uint32x4_t sum_u32[2] = { vdupq_n_u32(0), vdupq_n_u32(0) }; |
| |
| int w = width; |
| const uint16_t *row = src; |
| while (w >= 32) { |
| uint16x8_t s0 = vld1q_u16(row + 0); |
| uint16x8_t s1 = vld1q_u16(row + 8); |
| uint16x8_t s2 = vld1q_u16(row + 16); |
| uint16x8_t s3 = vld1q_u16(row + 24); |
| |
| s0 = vaddq_u16(s0, s1); |
| s2 = vaddq_u16(s2, s3); |
| sum_u32[0] = vpadalq_u16(sum_u32[0], s0); |
| sum_u32[1] = vpadalq_u16(sum_u32[1], s2); |
| |
| row += 32; |
| w -= 32; |
| } |
| |
| if (w >= 16) { |
| uint16x8_t s0 = vld1q_u16(row + 0); |
| uint16x8_t s1 = vld1q_u16(row + 8); |
| |
| s0 = vaddq_u16(s0, s1); |
| sum_u32[0] = vpadalq_u16(sum_u32[0], s0); |
| |
| row += 16; |
| w -= 16; |
| } |
| |
| if (w >= 8) { |
| uint16x8_t s0 = vld1q_u16(row); |
| sum_u32[1] = vpadalq_u16(sum_u32[1], s0); |
| |
| row += 8; |
| w -= 8; |
| } |
| |
| if (w >= 4) { |
| uint16x8_t s0 = vcombine_u16(vld1_u16(row), vdup_n_u16(0)); |
| sum_u32[0] = vpadalq_u16(sum_u32[0], s0); |
| |
| row += 4; |
| w -= 4; |
| } |
| |
| while (w-- > 0) { |
| sum += *row++; |
| } |
| |
| sum_u64 = vpadalq_u32(sum_u64, vaddq_u32(sum_u32[0], sum_u32[1])); |
| |
| src += src_stride; |
| } while (--h != 0); |
| |
| return (uint16_t)((horizontal_add_u64x2(sum_u64) + sum) / (height * width)); |
| } |
| |
| void av1_compute_stats_highbd_neon(int wiener_win, const uint8_t *dgd8, |
| const uint8_t *src8, int16_t *dgd_avg, |
| int16_t *src_avg, int h_start, int h_end, |
| int v_start, int v_end, int dgd_stride, |
| int src_stride, int64_t *M, int64_t *H, |
| aom_bit_depth_t bit_depth) { |
| (void)dgd_avg; |
| (void)src_avg; |
| assert(wiener_win == WIENER_WIN || wiener_win == WIENER_WIN_REDUCED); |
| |
| const int wiener_halfwin = wiener_win >> 1; |
| const uint16_t *src = CONVERT_TO_SHORTPTR(src8); |
| const uint16_t *dgd = CONVERT_TO_SHORTPTR(dgd8); |
| const int height = v_end - v_start; |
| const int width = h_end - h_start; |
| |
| const uint16_t *dgd_start = dgd + h_start + v_start * dgd_stride; |
| const uint16_t *src_start = src + h_start + v_start * src_stride; |
| |
| // The wiener window will slide along the dgd frame, centered on each pixel. |
| // For the top left pixel and all the pixels on the side of the frame this |
| // means half of the window will be outside of the frame. As such the actual |
| // buffer that we need to subtract the avg from will be 2 * wiener_halfwin |
| // wider and 2 * wiener_halfwin higher than the original dgd buffer. |
| const int vert_offset = v_start - wiener_halfwin; |
| const int horiz_offset = h_start - wiener_halfwin; |
| const uint16_t *dgd_win = dgd + horiz_offset + vert_offset * dgd_stride; |
| |
| uint16_t avg = highbd_find_average_neon(dgd_start, dgd_stride, width, height); |
| |
| if (wiener_win == WIENER_WIN) { |
| highbd_compute_stats_win7_neon(dgd_win, src_start, avg, width, height, |
| dgd_stride, src_stride, M, H, bit_depth); |
| } else { |
| highbd_compute_stats_win5_neon(dgd_win, src_start, avg, width, height, |
| dgd_stride, src_stride, M, H, bit_depth); |
| } |
| } |
| |
| int64_t av1_highbd_pixel_proj_error_neon( |
| const uint8_t *src8, int width, int height, int src_stride, |
| const uint8_t *dat8, int dat_stride, int32_t *flt0, int flt0_stride, |
| int32_t *flt1, int flt1_stride, int xq[2], const sgr_params_type *params) { |
| const uint16_t *src = CONVERT_TO_SHORTPTR(src8); |
| const uint16_t *dat = CONVERT_TO_SHORTPTR(dat8); |
| int64_t sse = 0; |
| int64x2_t sse_s64 = vdupq_n_s64(0); |
| |
| if (params->r[0] > 0 && params->r[1] > 0) { |
| int32x2_t xq_v = vld1_s32(xq); |
| int32x2_t xq_sum_v = vshl_n_s32(vpadd_s32(xq_v, xq_v), 4); |
| |
| do { |
| int j = 0; |
| int32x4_t sse_s32 = vdupq_n_s32(0); |
| |
| do { |
| const uint16x8_t d = vld1q_u16(&dat[j]); |
| const uint16x8_t s = vld1q_u16(&src[j]); |
| int32x4_t flt0_0 = vld1q_s32(&flt0[j]); |
| int32x4_t flt0_1 = vld1q_s32(&flt0[j + 4]); |
| int32x4_t flt1_0 = vld1q_s32(&flt1[j]); |
| int32x4_t flt1_1 = vld1q_s32(&flt1[j + 4]); |
| |
| int32x4_t d_s32_lo = vreinterpretq_s32_u32( |
| vmull_lane_u16(vget_low_u16(d), vreinterpret_u16_s32(xq_sum_v), 0)); |
| int32x4_t d_s32_hi = vreinterpretq_s32_u32(vmull_lane_u16( |
| vget_high_u16(d), vreinterpret_u16_s32(xq_sum_v), 0)); |
| |
| int32x4_t v0 = vsubq_s32( |
| vdupq_n_s32(1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)), |
| d_s32_lo); |
| int32x4_t v1 = vsubq_s32( |
| vdupq_n_s32(1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)), |
| d_s32_hi); |
| |
| v0 = vmlaq_lane_s32(v0, flt0_0, xq_v, 0); |
| v1 = vmlaq_lane_s32(v1, flt0_1, xq_v, 0); |
| v0 = vmlaq_lane_s32(v0, flt1_0, xq_v, 1); |
| v1 = vmlaq_lane_s32(v1, flt1_1, xq_v, 1); |
| |
| int16x4_t vr0 = vshrn_n_s32(v0, SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS); |
| int16x4_t vr1 = vshrn_n_s32(v1, SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS); |
| |
| int16x8_t e = vaddq_s16(vcombine_s16(vr0, vr1), |
| vreinterpretq_s16_u16(vsubq_u16(d, s))); |
| int16x4_t e_lo = vget_low_s16(e); |
| int16x4_t e_hi = vget_high_s16(e); |
| |
| sse_s32 = vmlal_s16(sse_s32, e_lo, e_lo); |
| sse_s32 = vmlal_s16(sse_s32, e_hi, e_hi); |
| |
| j += 8; |
| } while (j <= width - 8); |
| |
| for (int k = j; k < width; ++k) { |
| int32_t v = 1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1); |
| v += xq[0] * (flt0[k]) + xq[1] * (flt1[k]); |
| v -= (xq[1] + xq[0]) * (int32_t)(dat[k] << 4); |
| int32_t e = |
| (v >> (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS)) + dat[k] - src[k]; |
| sse += ((int64_t)e * e); |
| } |
| |
| sse_s64 = vpadalq_s32(sse_s64, sse_s32); |
| |
| dat += dat_stride; |
| src += src_stride; |
| flt0 += flt0_stride; |
| flt1 += flt1_stride; |
| } while (--height != 0); |
| } else if (params->r[0] > 0 || params->r[1] > 0) { |
| int xq_active = (params->r[0] > 0) ? xq[0] : xq[1]; |
| int32_t *flt = (params->r[0] > 0) ? flt0 : flt1; |
| int flt_stride = (params->r[0] > 0) ? flt0_stride : flt1_stride; |
| int32x4_t xq_v = vdupq_n_s32(xq_active); |
| |
| do { |
| int j = 0; |
| int32x4_t sse_s32 = vdupq_n_s32(0); |
| do { |
| const uint16x8_t d0 = vld1q_u16(&dat[j]); |
| const uint16x8_t s0 = vld1q_u16(&src[j]); |
| int32x4_t flt0_0 = vld1q_s32(&flt[j]); |
| int32x4_t flt0_1 = vld1q_s32(&flt[j + 4]); |
| |
| uint16x8_t d_u16 = vshlq_n_u16(d0, 4); |
| int32x4_t sub0 = vreinterpretq_s32_u32( |
| vsubw_u16(vreinterpretq_u32_s32(flt0_0), vget_low_u16(d_u16))); |
| int32x4_t sub1 = vreinterpretq_s32_u32( |
| vsubw_u16(vreinterpretq_u32_s32(flt0_1), vget_high_u16(d_u16))); |
| |
| int32x4_t v0 = vmlaq_s32( |
| vdupq_n_s32(1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)), sub0, |
| xq_v); |
| int32x4_t v1 = vmlaq_s32( |
| vdupq_n_s32(1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)), sub1, |
| xq_v); |
| |
| int16x4_t vr0 = vshrn_n_s32(v0, SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS); |
| int16x4_t vr1 = vshrn_n_s32(v1, SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS); |
| |
| int16x8_t e = vaddq_s16(vcombine_s16(vr0, vr1), |
| vreinterpretq_s16_u16(vsubq_u16(d0, s0))); |
| int16x4_t e_lo = vget_low_s16(e); |
| int16x4_t e_hi = vget_high_s16(e); |
| |
| sse_s32 = vmlal_s16(sse_s32, e_lo, e_lo); |
| sse_s32 = vmlal_s16(sse_s32, e_hi, e_hi); |
| |
| j += 8; |
| } while (j <= width - 8); |
| |
| for (int k = j; k < width; ++k) { |
| int32_t v = 1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1); |
| v += xq_active * (int32_t)((uint32_t)flt[j] - (uint16_t)(dat[k] << 4)); |
| const int32_t e = |
| (v >> (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS)) + dat[k] - src[k]; |
| sse += ((int64_t)e * e); |
| } |
| |
| sse_s64 = vpadalq_s32(sse_s64, sse_s32); |
| |
| dat += dat_stride; |
| flt += flt_stride; |
| src += src_stride; |
| } while (--height != 0); |
| } else { |
| do { |
| int j = 0; |
| |
| do { |
| const uint16x8_t d = vld1q_u16(&dat[j]); |
| const uint16x8_t s = vld1q_u16(&src[j]); |
| |
| uint16x8_t diff = vabdq_u16(d, s); |
| uint16x4_t diff_lo = vget_low_u16(diff); |
| uint16x4_t diff_hi = vget_high_u16(diff); |
| |
| uint32x4_t sqr_lo = vmull_u16(diff_lo, diff_lo); |
| uint32x4_t sqr_hi = vmull_u16(diff_hi, diff_hi); |
| |
| sse_s64 = vpadalq_s32(sse_s64, vreinterpretq_s32_u32(sqr_lo)); |
| sse_s64 = vpadalq_s32(sse_s64, vreinterpretq_s32_u32(sqr_hi)); |
| |
| j += 8; |
| } while (j <= width - 8); |
| |
| for (int k = j; k < width; ++k) { |
| int32_t e = dat[k] - src[k]; |
| sse += e * e; |
| } |
| |
| dat += dat_stride; |
| src += src_stride; |
| } while (--height != 0); |
| } |
| |
| sse += horizontal_add_s64x2(sse_s64); |
| return sse; |
| } |
