/* * Copyright (c) 2020, 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 #include "config/aom_config.h" #include "config/av1_rtcd.h" #include "aom_dsp/arm/sum_neon.h" #include "av1/common/restoration.h" #include "av1/encoder/arm/neon/pickrst_neon.h" #include "av1/encoder/pickrst.h" int64_t av1_lowbd_pixel_proj_error_neon( const uint8_t *src, int width, int height, int src_stride, const uint8_t *dat, int dat_stride, int32_t *flt0, int flt0_stride, int32_t *flt1, int flt1_stride, int xq[2], const sgr_params_type *params) { 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), SGRPROJ_RST_BITS); do { int j = 0; int32x4_t sse_s32 = vdupq_n_s32(0); do { const uint8x8_t d = vld1_u8(&dat[j]); const uint8x8_t s = vld1_u8(&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 offset = vdupq_n_s32(1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)); int32x4_t v0 = vmlaq_lane_s32(offset, flt0_0, xq_v, 0); int32x4_t v1 = vmlaq_lane_s32(offset, 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); int16x8_t d_s16 = vreinterpretq_s16_u16(vmovl_u8(d)); v0 = vmlsl_lane_s16(v0, vget_low_s16(d_s16), vreinterpret_s16_s32(xq_sum_v), 0); v1 = vmlsl_lane_s16(v1, vget_high_s16(d_s16), vreinterpret_s16_s32(xq_sum_v), 0); 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 diff = vreinterpretq_s16_u16(vsubl_u8(d, s)); int16x8_t e = vaddq_s16(vcombine_s16(vr0, vr1), diff); 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 u = (dat[k] << SGRPROJ_RST_BITS); int32_t v = (1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)) + xq[0] * flt0[k] + xq[1] * flt1[k] - u * (xq[0] + xq[1]); int32_t e = (v >> (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS)) + dat[k] - src[k]; sse += 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; int32x2_t xq_v = vdup_n_s32(xq_active); do { int32x4_t sse_s32 = vdupq_n_s32(0); int j = 0; do { const uint8x8_t d = vld1_u8(&dat[j]); const uint8x8_t s = vld1_u8(&src[j]); int32x4_t flt_0 = vld1q_s32(&flt[j]); int32x4_t flt_1 = vld1q_s32(&flt[j + 4]); int16x8_t d_s16 = vreinterpretq_s16_u16(vshll_n_u8(d, SGRPROJ_RST_BITS)); int32x4_t sub_0 = vsubw_s16(flt_0, vget_low_s16(d_s16)); int32x4_t sub_1 = vsubw_s16(flt_1, vget_high_s16(d_s16)); int32x4_t offset = vdupq_n_s32(1 << (SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS - 1)); int32x4_t v0 = vmlaq_lane_s32(offset, sub_0, xq_v, 0); int32x4_t v1 = vmlaq_lane_s32(offset, sub_1, xq_v, 0); 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 diff = vreinterpretq_s16_u16(vsubl_u8(d, s)); int16x8_t e = vaddq_s16(vcombine_s16(vr0, vr1), diff); 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 u = dat[k] << SGRPROJ_RST_BITS; int32_t v = xq_active * (flt[k] - u); int32_t e = ROUND_POWER_OF_TWO(v, SGRPROJ_RST_BITS + SGRPROJ_PRJ_BITS) + dat[k] - src[k]; sse += e * e; } sse_s64 = vpadalq_s32(sse_s64, sse_s32); dat += dat_stride; src += src_stride; flt += flt_stride; } while (--height != 0); } else { uint32x4_t sse_s32 = vdupq_n_u32(0); do { int j = 0; do { const uint8x16_t d = vld1q_u8(&dat[j]); const uint8x16_t s = vld1q_u8(&src[j]); uint8x16_t diff = vabdq_u8(d, s); uint8x8_t diff_lo = vget_low_u8(diff); uint8x8_t diff_hi = vget_high_u8(diff); sse_s32 = vpadalq_u16(sse_s32, vmull_u8(diff_lo, diff_lo)); sse_s32 = vpadalq_u16(sse_s32, vmull_u8(diff_hi, diff_hi)); j += 16; } while (j <= width - 16); 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_s64 = vreinterpretq_s64_u64(vpaddlq_u32(sse_s32)); } sse += horizontal_add_s64x2(sse_s64); return sse; } // We can accumulate up to 65536 8-bit multiplication results in 32-bit. We are // processing 2 pixels at a time, so the accumulator max can be as high as 32768 // for the compute stats. #define STAT_ACCUMULATOR_MAX 32768 static INLINE uint8x8_t tbl2(uint8x16_t a, uint8x16_t b, uint8x8_t idx) { #if AOM_ARCH_AARCH64 uint8x16x2_t table = { { a, b } }; return vqtbl2_u8(table, idx); #else uint8x8x4_t table = { { vget_low_u8(a), vget_high_u8(a), vget_low_u8(b), vget_high_u8(b) } }; return vtbl4_u8(table, idx); #endif } static INLINE uint8x16_t tbl2q(uint8x16_t a, uint8x16_t b, uint8x16_t idx) { #if AOM_ARCH_AARCH64 uint8x16x2_t table = { { a, b } }; return vqtbl2q_u8(table, idx); #else uint8x8x4_t table = { { vget_low_u8(a), vget_high_u8(a), vget_low_u8(b), vget_high_u8(b) } }; return vcombine_u8(vtbl4_u8(table, vget_low_u8(idx)), vtbl4_u8(table, vget_high_u8(idx))); #endif } // The M matrix is accumulated in STAT_ACCUMULATOR_MAX 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 scale) { for (int i = 0; i < wiener_win; ++i) { for (int j = 0; j < wiener_win; ++j) { int tr_idx = j * wiener_win + i; *dst++ += (int64_t)(src_s64[tr_idx] + src_s32[tr_idx]) * scale; } } } // 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 STAT_ACCUMULATOR_MAX 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 void update_H(int64_t *dst, const int64_t *src_s64, const int32_t *src_s32, const int wiener_win, int stride, int scale) { // 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-triangle indices, 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++ += (int64_t)(src_s64[tr_idx] + src_s32[tr_idx]) * scale; } } } } } // Load 7x7 matrix into 3 and a half 128-bit vectors from consecutive rows, the // last load address is offset to prevent out-of-bounds access. static INLINE void load_and_pack_u8_8x7(uint8x16_t dst[4], const uint8_t *src, ptrdiff_t stride) { dst[0] = vcombine_u8(vld1_u8(src), vld1_u8(src + stride)); src += 2 * stride; dst[1] = vcombine_u8(vld1_u8(src), vld1_u8(src + stride)); src += 2 * stride; dst[2] = vcombine_u8(vld1_u8(src), vld1_u8(src + stride)); src += 2 * stride; dst[3] = vcombine_u8(vld1_u8(src - 1), vdup_n_u8(0)); } static INLINE void compute_stats_win7_neon(const uint8_t *dgd, const uint8_t *src, int width, int height, int dgd_stride, int src_stride, int avg, int64_t *M, int64_t *H, int downsample_factor) { // 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[96]) = { 0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, }; // clang-format on const uint8x16_t lut0 = vld1q_u8(shuffle_stats7 + 0); const uint8x16_t lut1 = vld1q_u8(shuffle_stats7 + 16); const uint8x16_t lut2 = vld1q_u8(shuffle_stats7 + 32); const uint8x16_t lut3 = vld1q_u8(shuffle_stats7 + 48); const uint8x16_t lut4 = vld1q_u8(shuffle_stats7 + 64); const uint8x16_t lut5 = vld1q_u8(shuffle_stats7 + 80); int acc_cnt = STAT_ACCUMULATOR_MAX; const int src_next = downsample_factor * src_stride - width; const int dgd_next = downsample_factor * dgd_stride - width; const uint8x8_t avg_u8 = vdup_n_u8(avg); do { int j = width; while (j >= 2) { // Load two adjacent, overlapping 7x7 matrices: a 8x7 matrix with the // middle 6x7 elements being shared. uint8x16_t dgd_rows[4]; load_and_pack_u8_8x7(dgd_rows, dgd, dgd_stride); const uint8_t *dgd_ptr = dgd + dgd_stride * 6; dgd += 2; // Re-arrange (and widen) 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]; uint8x16_t dgd_shuf0 = tbl2q(dgd_rows[0], dgd_rows[1], lut0); uint8x16_t dgd_shuf3 = tbl2q(dgd_rows[0], dgd_rows[1], lut3); dgd_avg0[0] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf0), avg_u8)); dgd_avg0[1] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf0), avg_u8)); dgd_avg1[0] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf3), avg_u8)); dgd_avg1[1] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf3), avg_u8)); vst1q_s16(DGD_AVG0, dgd_avg0[0]); vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); vst1q_s16(DGD_AVG1, dgd_avg1[0]); vst1q_s16(DGD_AVG1 + 8, dgd_avg1[1]); uint8x16_t dgd_shuf1 = tbl2q(dgd_rows[1], dgd_rows[2], lut1); uint8x16_t dgd_shuf4 = tbl2q(dgd_rows[1], dgd_rows[2], lut4); dgd_avg0[2] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf1), avg_u8)); dgd_avg0[3] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf1), avg_u8)); dgd_avg1[2] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf4), avg_u8)); dgd_avg1[3] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf4), avg_u8)); vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); vst1q_s16(DGD_AVG0 + 24, dgd_avg0[3]); vst1q_s16(DGD_AVG1 + 16, dgd_avg1[2]); vst1q_s16(DGD_AVG1 + 24, dgd_avg1[3]); uint8x16_t dgd_shuf2 = tbl2q(dgd_rows[2], dgd_rows[3], lut2); uint8x16_t dgd_shuf5 = tbl2q(dgd_rows[2], dgd_rows[3], lut5); dgd_avg0[4] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf2), avg_u8)); dgd_avg0[5] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf2), avg_u8)); dgd_avg1[4] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf5), avg_u8)); dgd_avg1[5] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf5), avg_u8)); vst1q_s16(DGD_AVG0 + 32, dgd_avg0[4]); vst1q_s16(DGD_AVG0 + 40, dgd_avg0[5]); vst1q_s16(DGD_AVG1 + 32, dgd_avg1[4]); 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 STAT_ACCUMULATOR_MAX iterations to prevent // overflow. if (--acc_cnt == 0) { acc_cnt = STAT_ACCUMULATOR_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. uint8x16_t dgd_rows[4]; load_and_pack_u8_8x7(dgd_rows, dgd, dgd_stride); const uint8_t *dgd_ptr = dgd + dgd_stride * 6; ++dgd; // Re-arrange (and widen) 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]; uint8x16_t dgd_shuf0 = tbl2q(dgd_rows[0], dgd_rows[1], lut0); dgd_avg0[0] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf0), avg_u8)); dgd_avg0[1] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf0), avg_u8)); vst1q_s16(DGD_AVG0, dgd_avg0[0]); vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); uint8x16_t dgd_shuf1 = tbl2q(dgd_rows[1], dgd_rows[2], lut1); dgd_avg0[2] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf1), avg_u8)); dgd_avg0[3] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf1), avg_u8)); vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); vst1q_s16(DGD_AVG0 + 24, dgd_avg0[3]); uint8x16_t dgd_shuf2 = tbl2q(dgd_rows[2], dgd_rows[3], lut2); dgd_avg0[4] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf2), avg_u8)); dgd_avg0[5] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf2), avg_u8)); 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); acc_transpose_M(M, M_s64, M_s32, WIENER_WIN, downsample_factor); update_H(H, H_s64, H_s32, WIENER_WIN, WIENER_WIN2_ALIGN2, downsample_factor); } // Load 5x5 matrix into 2 and a half 128-bit vectors from consecutive rows, the // last load address is offset to prevent out-of-bounds access. static INLINE void load_and_pack_u8_6x5(uint8x16_t dst[3], const uint8_t *src, ptrdiff_t stride) { dst[0] = vcombine_u8(vld1_u8(src), vld1_u8(src + stride)); src += 2 * stride; dst[1] = vcombine_u8(vld1_u8(src), vld1_u8(src + stride)); src += 2 * stride; dst[2] = vcombine_u8(vld1_u8(src - 3), vdup_n_u8(0)); } static INLINE void compute_stats_win5_neon(const uint8_t *dgd, const uint8_t *src, int width, int height, int dgd_stride, int src_stride, int avg, int64_t *M, int64_t *H, int downsample_factor) { // 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 two 5x5 // matrices. // clang-format off DECLARE_ALIGNED(16, static const uint8_t, shuffle_stats5[48]) = { 0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 16, 17, 18, 19, 20, 24, 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 25, 9, 10, 11, 12, 19, 20, 21, 22, 10, 11, 12, 13, 20, 21, 22, 23, }; // clang-format on const uint8x16_t lut0 = vld1q_u8(shuffle_stats5 + 0); const uint8x16_t lut1 = vld1q_u8(shuffle_stats5 + 16); const uint8x16_t lut2 = vld1q_u8(shuffle_stats5 + 32); int acc_cnt = STAT_ACCUMULATOR_MAX; const int src_next = downsample_factor * src_stride - width; const int dgd_next = downsample_factor * dgd_stride - width; const uint8x8_t avg_u8 = vdup_n_u8(avg); do { int j = width; while (j >= 2) { // Load two adjacent, overlapping 5x5 matrices: a 6x5 matrix with the // middle 4x5 elements being shared. uint8x16_t dgd_rows[3]; load_and_pack_u8_6x5(dgd_rows, dgd, dgd_stride); const uint8_t *dgd_ptr = dgd + dgd_stride * 4; dgd += 2; // Re-arrange (and widen) 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]; uint8x16_t dgd_shuf0 = tbl2q(dgd_rows[0], dgd_rows[1], lut0); uint8x16_t dgd_shuf1 = tbl2q(dgd_rows[0], dgd_rows[1], lut1); uint8x16_t dgd_shuf2 = tbl2q(dgd_rows[1], dgd_rows[2], lut2); dgd_avg0[0] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf0), avg_u8)); dgd_avg0[1] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf0), avg_u8)); dgd_avg0[2] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf2), avg_u8)); dgd_avg1[0] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf1), avg_u8)); dgd_avg1[1] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf1), avg_u8)); dgd_avg1[2] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf2), avg_u8)); vst1q_s16(DGD_AVG0 + 0, dgd_avg0[0]); vst1q_s16(DGD_AVG0 + 8, dgd_avg0[1]); vst1q_s16(DGD_AVG0 + 16, dgd_avg0[2]); vst1q_s16(DGD_AVG1 + 0, dgd_avg1[0]); vst1q_s16(DGD_AVG1 + 8, dgd_avg1[1]); 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 STAT_ACCUMULATOR_MAX iterations to prevent // overflow. if (--acc_cnt == 0) { acc_cnt = STAT_ACCUMULATOR_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. uint8x16_t dgd_rows[3]; load_and_pack_u8_6x5(dgd_rows, dgd, dgd_stride); const uint8_t *dgd_ptr = 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]; uint8x16_t dgd_shuf0 = tbl2q(dgd_rows[0], dgd_rows[1], lut0); uint8x8_t dgd_shuf1 = tbl2(dgd_rows[1], dgd_rows[2], vget_low_u8(lut2)); dgd_avg0[0] = vreinterpretq_s16_u16(vsubl_u8(vget_low_u8(dgd_shuf0), avg_u8)); dgd_avg0[1] = vreinterpretq_s16_u16(vsubl_u8(vget_high_u8(dgd_shuf0), avg_u8)); dgd_avg0[2] = vreinterpretq_s16_u16(vsubl_u8(dgd_shuf1, avg_u8)); vst1q_s16(DGD_AVG0 + 0, 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; // 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 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); acc_transpose_M(M, M_s64, M_s32, WIENER_WIN_REDUCED, downsample_factor); update_H(H, H_s64, H_s32, WIENER_WIN_REDUCED, WIENER_WIN2_REDUCED_ALIGN2, downsample_factor); } static INLINE uint8_t find_average_neon(const uint8_t *src, int src_stride, int width, int height) { uint64_t sum = 0; if (width >= 16) { int h = 0; // We can accumulate up to 257 8-bit values in a 16-bit value, given // that each 16-bit vector has 8 elements, that means we can process up to // int(257*8/width) rows before we need to widen to 32-bit vector // elements. int h_overflow = 257 * 8 / width; int h_limit = height > h_overflow ? h_overflow : height; uint32x4_t avg_u32 = vdupq_n_u32(0); do { uint16x8_t avg_u16 = vdupq_n_u16(0); do { int j = width; const uint8_t *src_ptr = src; do { uint8x16_t s = vld1q_u8(src_ptr); avg_u16 = vpadalq_u8(avg_u16, s); j -= 16; src_ptr += 16; } while (j >= 16); if (j >= 8) { uint8x8_t s = vld1_u8(src_ptr); avg_u16 = vaddw_u8(avg_u16, s); j -= 8; src_ptr += 8; } // Scalar tail case. while (j > 0) { sum += src[width - j]; j--; } src += src_stride; } while (++h < h_limit); avg_u32 = vpadalq_u16(avg_u32, avg_u16); h_limit += h_overflow; h_limit = height > h_overflow ? h_overflow : height; } while (h < height); return (uint8_t)((horizontal_long_add_u32x4(avg_u32) + sum) / (width * height)); } if (width >= 8) { int h = 0; // We can accumulate up to 257 8-bit values in a 16-bit value, given // that each 16-bit vector has 4 elements, that means we can process up to // int(257*4/width) rows before we need to widen to 32-bit vector // elements. int h_overflow = 257 * 4 / width; int h_limit = height > h_overflow ? h_overflow : height; uint32x2_t avg_u32 = vdup_n_u32(0); do { uint16x4_t avg_u16 = vdup_n_u16(0); do { int j = width; const uint8_t *src_ptr = src; uint8x8_t s = vld1_u8(src_ptr); avg_u16 = vpadal_u8(avg_u16, s); j -= 8; src_ptr += 8; // Scalar tail case. while (j > 0) { sum += src[width - j]; j--; } src += src_stride; } while (++h < h_limit); avg_u32 = vpadal_u16(avg_u32, avg_u16); h_limit += h_overflow; h_limit = height > h_overflow ? h_overflow : height; } while (h < height); return (uint8_t)((horizontal_long_add_u32x2(avg_u32) + sum) / (width * height)); } int i = height; do { int j = 0; do { sum += src[j]; } while (++j < width); src += src_stride; } while (--i != 0); return (uint8_t)(sum / (width * height)); } void av1_compute_stats_neon(int wiener_win, const uint8_t *dgd, const uint8_t *src, 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, int use_downsampled_wiener_stats) { assert(wiener_win == WIENER_WIN || wiener_win == WIENER_WIN_CHROMA); assert(WIENER_STATS_DOWNSAMPLE_FACTOR == 4); (void)dgd_avg; (void)src_avg; const int wiener_win2 = wiener_win * wiener_win; const int wiener_halfwin = wiener_win >> 1; const int width = h_end - h_start; const int height = v_end - v_start; const uint8_t *dgd_start = dgd + h_start + v_start * dgd_stride; const uint8_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 uint8_t *dgd_win = dgd + horiz_offset + vert_offset * dgd_stride; uint8_t avg = find_average_neon(dgd_start, dgd_stride, width, height); // Since the height is not necessarily a multiple of the downsample factor, // the last line of src will be scaled according to how many rows remain. int downsample_factor = use_downsampled_wiener_stats ? WIENER_STATS_DOWNSAMPLE_FACTOR : 1; int downsampled_height = height / downsample_factor; int downsample_remainder = height % downsample_factor; memset(M, 0, wiener_win2 * sizeof(*M)); memset(H, 0, wiener_win2 * wiener_win2 * sizeof(*H)); // Calculate the M and H matrices for the normal and downsampled cases. if (downsampled_height > 0) { if (wiener_win == WIENER_WIN) { compute_stats_win7_neon(dgd_win, src_start, width, downsampled_height, dgd_stride, src_stride, avg, M, H, downsample_factor); } else { compute_stats_win5_neon(dgd_win, src_start, width, downsampled_height, dgd_stride, src_stride, avg, M, H, downsample_factor); } } // Accumulate the remaining last rows in the downsampled case. if (downsample_remainder > 0) { int remainder_offset = height - downsample_remainder; if (wiener_win == WIENER_WIN) { compute_stats_win7_neon(dgd_win + remainder_offset * dgd_stride, src_start + remainder_offset * src_stride, width, 1, dgd_stride, src_stride, avg, M, H, downsample_remainder); } else { compute_stats_win5_neon(dgd_win + remainder_offset * dgd_stride, src_start + remainder_offset * src_stride, width, 1, dgd_stride, src_stride, avg, M, H, downsample_remainder); } } } static INLINE void 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; 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 uint8_t *src_ptr = src8; const uint8_t *dat_ptr = dat8; int32_t *flt0_ptr = flt0; int32_t *flt1_ptr = flt1; int w = width; do { uint8x8_t s = vld1_u8(src_ptr); uint8x8_t d = vld1_u8(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); int16x8_t u = vreinterpretq_s16_u16(vshll_n_u8(d, SGRPROJ_RST_BITS)); int16x8_t s_s16 = vreinterpretq_s16_u16(vshll_n_u8(s, SGRPROJ_RST_BITS)); int32x4_t s_lo = vsubl_s16(vget_low_s16(s_s16), vget_low_s16(u)); int32x4_t s_hi = vsubl_s16(vget_high_s16(s_s16), vget_high_s16(u)); f0_lo = vsubw_s16(f0_lo, vget_low_s16(u)); f0_hi = vsubw_s16(f0_hi, vget_high_s16(u)); f1_lo = vsubw_s16(f1_lo, vget_low_s16(u)); f1_hi = vsubw_s16(f1_hi, vget_high_s16(u)); 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); src8 += src_stride; dat8 += 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 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; 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 uint8_t *src_ptr = src8; const uint8_t *dat_ptr = dat8; int32_t *flt0_ptr = flt0; int w = width; do { uint8x8_t s = vld1_u8(src_ptr); uint8x8_t d = vld1_u8(dat_ptr); int32x4_t f0_lo = vld1q_s32(flt0_ptr); int32x4_t f0_hi = vld1q_s32(flt0_ptr + 4); int16x8_t u = vreinterpretq_s16_u16(vshll_n_u8(d, SGRPROJ_RST_BITS)); int16x8_t s_s16 = vreinterpretq_s16_u16(vshll_n_u8(s, SGRPROJ_RST_BITS)); int32x4_t s_lo = vsubl_s16(vget_low_s16(s_s16), vget_low_s16(u)); int32x4_t s_hi = vsubl_s16(vget_high_s16(s_s16), vget_high_s16(u)); f0_lo = vsubw_s16(f0_lo, vget_low_s16(u)); f0_hi = vsubw_s16(f0_hi, vget_high_s16(u)); 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); src8 += src_stride; dat8 += 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 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; 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 uint8_t *src_ptr = src8; const uint8_t *dat_ptr = dat8; int32_t *flt1_ptr = flt1; int w = width; do { uint8x8_t s = vld1_u8(src_ptr); uint8x8_t d = vld1_u8(dat_ptr); int32x4_t f1_lo = vld1q_s32(flt1_ptr); int32x4_t f1_hi = vld1q_s32(flt1_ptr + 4); int16x8_t u = vreinterpretq_s16_u16(vshll_n_u8(d, SGRPROJ_RST_BITS)); int16x8_t s_s16 = vreinterpretq_s16_u16(vshll_n_u8(s, SGRPROJ_RST_BITS)); int32x4_t s_lo = vsubl_s16(vget_low_s16(s_s16), vget_low_s16(u)); int32x4_t s_hi = vsubl_s16(vget_high_s16(s_s16), vget_high_s16(u)); f1_lo = vsubw_s16(f1_lo, vget_low_s16(u)); f1_hi = vsubw_s16(f1_hi, vget_high_s16(u)); 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); src8 += src_stride; dat8 += 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_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)) { 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) { calc_proj_params_r0_neon(src8, width, height, src_stride, dat8, dat_stride, flt0, flt0_stride, H, C); } else if (params->r[1] > 0) { calc_proj_params_r1_neon(src8, width, height, src_stride, dat8, dat_stride, flt1, flt1_stride, H, C); } }