Merging upstream version 127.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

2 files changed, 594 insertions, 1 deletions

diff --git a/third_party/aom/av1/encoder/arm/neon/highbd_pickrst_neon.c b/third_party/aom/av1/encoder/arm/neon/highbd_pickrst_neon.c
index 47b5f5cfb7..8b0d3bcc7e 100644
--- a/third_party/aom/av1/encoder/arm/neon/highbd_pickrst_neon.c
+++ b/third_party/aom/av1/encoder/arm/neon/highbd_pickrst_neon.c
@@ -1008,10 +1008,13 @@ static uint16_t highbd_find_average_neon(const uint16_t *src, int src_stride,
 }
 
 void av1_compute_stats_highbd_neon(int wiener_win, const uint8_t *dgd8,
-                                   const uint8_t *src8, int h_start, int h_end,
+                                   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;
diff --git a/third_party/aom/av1/encoder/arm/neon/pickrst_sve.c b/third_party/aom/av1/encoder/arm/neon/pickrst_sve.c
new file mode 100644
index 0000000000..a519ecc5f5
--- /dev/null
+++ b/third_party/aom/av1/encoder/arm/neon/pickrst_sve.c
@@ -0,0 +1,590 @@
+/*
+ * Copyright (c) 2024, 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 <arm_sve.h>
+#include <string.h>
+
+#include "config/aom_config.h"
+#include "config/av1_rtcd.h"
+
+#include "aom_dsp/arm/aom_neon_sve_bridge.h"
+#include "aom_dsp/arm/mem_neon.h"
+#include "aom_dsp/arm/sum_neon.h"
+#include "aom_dsp/arm/transpose_neon.h"
+#include "av1/common/restoration.h"
+#include "av1/encoder/pickrst.h"
+
+static INLINE uint8_t find_average_sve(const uint8_t *src, int src_stride,
+                                       int width, int height) {
+  uint32x4_t avg_u32 = vdupq_n_u32(0);
+  uint8x16_t ones = vdupq_n_u8(1);
+
+  // Use a predicate to compute the last columns.
+  svbool_t pattern = svwhilelt_b8_u32(0, width % 16);
+
+  int h = height;
+  do {
+    int j = width;
+    const uint8_t *src_ptr = src;
+    while (j >= 16) {
+      uint8x16_t s = vld1q_u8(src_ptr);
+      avg_u32 = vdotq_u32(avg_u32, s, ones);
+
+      j -= 16;
+      src_ptr += 16;
+    }
+    uint8x16_t s_end = svget_neonq_u8(svld1_u8(pattern, src_ptr));
+    avg_u32 = vdotq_u32(avg_u32, s_end, ones);
+
+    src += src_stride;
+  } while (--h != 0);
+  return (uint8_t)(vaddlvq_u32(avg_u32) / (width * height));
+}
+
+static INLINE void compute_sub_avg(const uint8_t *buf, int buf_stride, int avg,
+                                   int16_t *buf_avg, int buf_avg_stride,
+                                   int width, int height,
+                                   int downsample_factor) {
+  uint8x8_t avg_u8 = vdup_n_u8(avg);
+
+  // Use a predicate to compute the last columns.
+  svbool_t pattern = svwhilelt_b8_u32(0, width % 8);
+
+  uint8x8_t avg_end = vget_low_u8(svget_neonq_u8(svdup_n_u8_z(pattern, avg)));
+
+  do {
+    int j = width;
+    const uint8_t *buf_ptr = buf;
+    int16_t *buf_avg_ptr = buf_avg;
+    while (j >= 8) {
+      uint8x8_t d = vld1_u8(buf_ptr);
+      vst1q_s16(buf_avg_ptr, vreinterpretq_s16_u16(vsubl_u8(d, avg_u8)));
+
+      j -= 8;
+      buf_ptr += 8;
+      buf_avg_ptr += 8;
+    }
+    uint8x8_t d_end = vget_low_u8(svget_neonq_u8(svld1_u8(pattern, buf_ptr)));
+    vst1q_s16(buf_avg_ptr, vreinterpretq_s16_u16(vsubl_u8(d_end, avg_end)));
+
+    buf += buf_stride;
+    buf_avg += buf_avg_stride;
+    height -= downsample_factor;
+  } while (height > 0);
+}
+
+static INLINE void copy_upper_triangle(int64_t *H, int64_t *H_tmp,
+                                       const int wiener_win2, const int scale) {
+  for (int i = 0; i < wiener_win2 - 2; i = i + 2) {
+    // Transpose the first 2x2 square. It needs a special case as the element
+    // of the bottom left is on the diagonal.
+    int64x2_t row0 = vld1q_s64(H_tmp + i * wiener_win2 + i + 1);
+    int64x2_t row1 = vld1q_s64(H_tmp + (i + 1) * wiener_win2 + i + 1);
+
+    int64x2_t tr_row = aom_vtrn2q_s64(row0, row1);
+
+    vst1_s64(H_tmp + (i + 1) * wiener_win2 + i, vget_low_s64(row0));
+    vst1q_s64(H_tmp + (i + 2) * wiener_win2 + i, tr_row);
+
+    // Transpose and store all the remaining 2x2 squares of the line.
+    for (int j = i + 3; j < wiener_win2; j = j + 2) {
+      row0 = vld1q_s64(H_tmp + i * wiener_win2 + j);
+      row1 = vld1q_s64(H_tmp + (i + 1) * wiener_win2 + j);
+
+      int64x2_t tr_row0 = aom_vtrn1q_s64(row0, row1);
+      int64x2_t tr_row1 = aom_vtrn2q_s64(row0, row1);
+
+      vst1q_s64(H_tmp + j * wiener_win2 + i, tr_row0);
+      vst1q_s64(H_tmp + (j + 1) * wiener_win2 + i, tr_row1);
+    }
+  }
+  for (int i = 0; i < wiener_win2 * wiener_win2; i++) {
+    H[i] += H_tmp[i] * scale;
+  }
+}
+
+// Transpose the matrix that has just been computed and accumulate it in M.
+static INLINE void acc_transpose_M(int64_t *M, const int64_t *M_trn,
+                                   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;
+      *M++ += (int64_t)(M_trn[tr_idx] * scale);
+    }
+  }
+}
+
+// Swap each half of the dgd vectors so that we can accumulate the result of
+// the dot-products directly in the destination matrix.
+static INLINE int16x8x2_t transpose_dgd(int16x8_t dgd0, int16x8_t dgd1) {
+  int16x8_t dgd_trn0 = vreinterpretq_s16_s64(
+      vzip1q_s64(vreinterpretq_s64_s16(dgd0), vreinterpretq_s64_s16(dgd1)));
+  int16x8_t dgd_trn1 = vreinterpretq_s16_s64(
+      vzip2q_s64(vreinterpretq_s64_s16(dgd0), vreinterpretq_s64_s16(dgd1)));
+
+  return (struct int16x8x2_t){ dgd_trn0, dgd_trn1 };
+}
+
+static INLINE void compute_M_one_row_win5(int16x8_t src, int16x8_t dgd[5],
+                                          int64_t *M, int row) {
+  const int wiener_win = 5;
+
+  int64x2_t m01 = vld1q_s64(M + row * wiener_win + 0);
+  int16x8x2_t dgd01 = transpose_dgd(dgd[0], dgd[1]);
+
+  int64x2_t cross_corr01 = aom_svdot_lane_s16(m01, dgd01.val[0], src, 0);
+  cross_corr01 = aom_svdot_lane_s16(cross_corr01, dgd01.val[1], src, 1);
+  vst1q_s64(M + row * wiener_win + 0, cross_corr01);
+
+  int64x2_t m23 = vld1q_s64(M + row * wiener_win + 2);
+  int16x8x2_t dgd23 = transpose_dgd(dgd[2], dgd[3]);
+
+  int64x2_t cross_corr23 = aom_svdot_lane_s16(m23, dgd23.val[0], src, 0);
+  cross_corr23 = aom_svdot_lane_s16(cross_corr23, dgd23.val[1], src, 1);
+  vst1q_s64(M + row * wiener_win + 2, cross_corr23);
+
+  int64x2_t m4 = aom_sdotq_s16(vdupq_n_s64(0), src, dgd[4]);
+  M[row * wiener_win + 4] += vaddvq_s64(m4);
+}
+
+static INLINE void compute_M_one_row_win7(int16x8_t src, int16x8_t dgd[7],
+                                          int64_t *M, int row) {
+  const int wiener_win = 7;
+
+  int64x2_t m01 = vld1q_s64(M + row * wiener_win + 0);
+  int16x8x2_t dgd01 = transpose_dgd(dgd[0], dgd[1]);
+
+  int64x2_t cross_corr01 = aom_svdot_lane_s16(m01, dgd01.val[0], src, 0);
+  cross_corr01 = aom_svdot_lane_s16(cross_corr01, dgd01.val[1], src, 1);
+  vst1q_s64(M + row * wiener_win + 0, cross_corr01);
+
+  int64x2_t m23 = vld1q_s64(M + row * wiener_win + 2);
+  int16x8x2_t dgd23 = transpose_dgd(dgd[2], dgd[3]);
+
+  int64x2_t cross_corr23 = aom_svdot_lane_s16(m23, dgd23.val[0], src, 0);
+  cross_corr23 = aom_svdot_lane_s16(cross_corr23, dgd23.val[1], src, 1);
+  vst1q_s64(M + row * wiener_win + 2, cross_corr23);
+
+  int64x2_t m45 = vld1q_s64(M + row * wiener_win + 4);
+  int16x8x2_t dgd45 = transpose_dgd(dgd[4], dgd[5]);
+
+  int64x2_t cross_corr45 = aom_svdot_lane_s16(m45, dgd45.val[0], src, 0);
+  cross_corr45 = aom_svdot_lane_s16(cross_corr45, dgd45.val[1], src, 1);
+  vst1q_s64(M + row * wiener_win + 4, cross_corr45);
+
+  int64x2_t m6 = aom_sdotq_s16(vdupq_n_s64(0), src, dgd[6]);
+  M[row * wiener_win + 6] += vaddvq_s64(m6);
+}
+
+static INLINE void compute_H_one_col(int16x8_t *dgd, int col, int64_t *H,
+                                     const int wiener_win,
+                                     const int wiener_win2) {
+  for (int row0 = 0; row0 < wiener_win; row0++) {
+    for (int row1 = row0; row1 < wiener_win; row1++) {
+      int auto_cov_idx =
+          (col * wiener_win + row0) * wiener_win2 + (col * wiener_win) + row1;
+
+      int64x2_t auto_cov = aom_sdotq_s16(vdupq_n_s64(0), dgd[row0], dgd[row1]);
+      H[auto_cov_idx] += vaddvq_s64(auto_cov);
+    }
+  }
+}
+
+static INLINE void compute_H_two_rows_win5(int16x8_t *dgd0, int16x8_t *dgd1,
+                                           int row0, int row1, int64_t *H) {
+  for (int col0 = 0; col0 < 5; col0++) {
+    int auto_cov_idx = (row0 * 5 + col0) * 25 + (row1 * 5);
+
+    int64x2_t h01 = vld1q_s64(H + auto_cov_idx);
+    int16x8x2_t dgd01 = transpose_dgd(dgd1[0], dgd1[1]);
+
+    int64x2_t auto_cov01 = aom_svdot_lane_s16(h01, dgd01.val[0], dgd0[col0], 0);
+    auto_cov01 = aom_svdot_lane_s16(auto_cov01, dgd01.val[1], dgd0[col0], 1);
+    vst1q_s64(H + auto_cov_idx, auto_cov01);
+
+    int64x2_t h23 = vld1q_s64(H + auto_cov_idx + 2);
+    int16x8x2_t dgd23 = transpose_dgd(dgd1[2], dgd1[3]);
+
+    int64x2_t auto_cov23 = aom_svdot_lane_s16(h23, dgd23.val[0], dgd0[col0], 0);
+    auto_cov23 = aom_svdot_lane_s16(auto_cov23, dgd23.val[1], dgd0[col0], 1);
+    vst1q_s64(H + auto_cov_idx + 2, auto_cov23);
+
+    int64x2_t auto_cov4 = aom_sdotq_s16(vdupq_n_s64(0), dgd0[col0], dgd1[4]);
+    H[auto_cov_idx + 4] += vaddvq_s64(auto_cov4);
+  }
+}
+
+static INLINE void compute_H_two_rows_win7(int16x8_t *dgd0, int16x8_t *dgd1,
+                                           int row0, int row1, int64_t *H) {
+  for (int col0 = 0; col0 < 7; col0++) {
+    int auto_cov_idx = (row0 * 7 + col0) * 49 + (row1 * 7);
+
+    int64x2_t h01 = vld1q_s64(H + auto_cov_idx);
+    int16x8x2_t dgd01 = transpose_dgd(dgd1[0], dgd1[1]);
+
+    int64x2_t auto_cov01 = aom_svdot_lane_s16(h01, dgd01.val[0], dgd0[col0], 0);
+    auto_cov01 = aom_svdot_lane_s16(auto_cov01, dgd01.val[1], dgd0[col0], 1);
+    vst1q_s64(H + auto_cov_idx, auto_cov01);
+
+    int64x2_t h23 = vld1q_s64(H + auto_cov_idx + 2);
+    int16x8x2_t dgd23 = transpose_dgd(dgd1[2], dgd1[3]);
+
+    int64x2_t auto_cov23 = aom_svdot_lane_s16(h23, dgd23.val[0], dgd0[col0], 0);
+    auto_cov23 = aom_svdot_lane_s16(auto_cov23, dgd23.val[1], dgd0[col0], 1);
+    vst1q_s64(H + auto_cov_idx + 2, auto_cov23);
+
+    int64x2_t h45 = vld1q_s64(H + auto_cov_idx + 4);
+    int16x8x2_t dgd45 = transpose_dgd(dgd1[4], dgd1[5]);
+
+    int64x2_t auto_cov45 = aom_svdot_lane_s16(h45, dgd45.val[0], dgd0[col0], 0);
+    auto_cov45 = aom_svdot_lane_s16(auto_cov45, dgd45.val[1], dgd0[col0], 1);
+    vst1q_s64(H + auto_cov_idx + 4, auto_cov45);
+
+    int64x2_t auto_cov6 = aom_sdotq_s16(vdupq_n_s64(0), dgd0[col0], dgd1[6]);
+    H[auto_cov_idx + 6] += vaddvq_s64(auto_cov6);
+  }
+}
+
+// This function computes two matrices: the cross-correlation between the src
+// buffer and dgd buffer (M), and the auto-covariance of the dgd buffer (H).
+//
+// 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
+// accesses, as it means we do contiguous loads of dgd but strided stores to M.
+// As a result, we use an intermediate matrix M_trn which is instead filled
+// such that one row of the wiener window gives one row of M_trn. Once fully
+// computed, M_trn is then transposed to return M.
+//
+// H is of size 49 * 49. It is filled by multiplying every pair of elements of
+// the wiener window together. Since it is a symmetric matrix, we only compute
+// the upper triangle, and then copy it down to the lower one. Here we fill it
+// by taking each different pair of columns, and multiplying all the elements of
+// the first one with all the elements of the second one, with a special case
+// when multiplying a column by itself.
+static INLINE void compute_stats_win7_sve(int16_t *dgd_avg, int dgd_avg_stride,
+                                          int16_t *src_avg, int src_avg_stride,
+                                          int width, int height, int64_t *M,
+                                          int64_t *H, int downsample_factor) {
+  const int wiener_win = 7;
+  const int wiener_win2 = wiener_win * wiener_win;
+
+  // Use a predicate to compute the last columns of the block for H.
+  svbool_t pattern = svwhilelt_b16_u32(0, width % 8);
+
+  // Use intermediate matrices for H and M to perform the computation, they
+  // will be accumulated into the original H and M at the end.
+  int64_t M_trn[49];
+  memset(M_trn, 0, sizeof(M_trn));
+
+  int64_t H_tmp[49 * 49];
+  memset(H_tmp, 0, sizeof(H_tmp));
+
+  do {
+    // Cross-correlation (M).
+    for (int row = 0; row < wiener_win; row++) {
+      int j = 0;
+      while (j < width) {
+        int16x8_t dgd[7];
+        load_s16_8x7(dgd_avg + row * dgd_avg_stride + j, 1, &dgd[0], &dgd[1],
+                     &dgd[2], &dgd[3], &dgd[4], &dgd[5], &dgd[6]);
+        int16x8_t s = vld1q_s16(src_avg + j);
+
+        // Compute all the elements of one row of M.
+        compute_M_one_row_win7(s, dgd, M_trn, row);
+
+        j += 8;
+      }
+    }
+
+    // Auto-covariance (H).
+    int j = 0;
+    while (j <= width - 8) {
+      for (int col0 = 0; col0 < wiener_win; col0++) {
+        int16x8_t dgd0[7];
+        load_s16_8x7(dgd_avg + j + col0, dgd_avg_stride, &dgd0[0], &dgd0[1],
+                     &dgd0[2], &dgd0[3], &dgd0[4], &dgd0[5], &dgd0[6]);
+
+        // Perform computation of the first column with itself (28 elements).
+        // For the first column this will fill the upper triangle of the 7x7
+        // matrix at the top left of the H matrix. For the next columns this
+        // will fill the upper triangle of the other 7x7 matrices around H's
+        // diagonal.
+        compute_H_one_col(dgd0, col0, H_tmp, wiener_win, wiener_win2);
+
+        // All computation next to the matrix diagonal has already been done.
+        for (int col1 = col0 + 1; col1 < wiener_win; col1++) {
+          // Load second column and scale based on downsampling factor.
+          int16x8_t dgd1[7];
+          load_s16_8x7(dgd_avg + j + col1, dgd_avg_stride, &dgd1[0], &dgd1[1],
+                       &dgd1[2], &dgd1[3], &dgd1[4], &dgd1[5], &dgd1[6]);
+
+          // Compute all elements from the combination of both columns (49
+          // elements).
+          compute_H_two_rows_win7(dgd0, dgd1, col0, col1, H_tmp);
+        }
+      }
+      j += 8;
+    }
+
+    if (j < width) {
+      // Process remaining columns using a predicate to discard excess elements.
+      for (int col0 = 0; col0 < wiener_win; col0++) {
+        // Load first column.
+        int16x8_t dgd0[7];
+        dgd0[0] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 0 * dgd_avg_stride + j + col0));
+        dgd0[1] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 1 * dgd_avg_stride + j + col0));
+        dgd0[2] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 2 * dgd_avg_stride + j + col0));
+        dgd0[3] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 3 * dgd_avg_stride + j + col0));
+        dgd0[4] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 4 * dgd_avg_stride + j + col0));
+        dgd0[5] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 5 * dgd_avg_stride + j + col0));
+        dgd0[6] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 6 * dgd_avg_stride + j + col0));
+
+        // Perform computation of the first column with itself (28 elements).
+        // For the first column this will fill the upper triangle of the 7x7
+        // matrix at the top left of the H matrix. For the next columns this
+        // will fill the upper triangle of the other 7x7 matrices around H's
+        // diagonal.
+        compute_H_one_col(dgd0, col0, H_tmp, wiener_win, wiener_win2);
+
+        // All computation next to the matrix diagonal has already been done.
+        for (int col1 = col0 + 1; col1 < wiener_win; col1++) {
+          // Load second column and scale based on downsampling factor.
+          int16x8_t dgd1[7];
+          load_s16_8x7(dgd_avg + j + col1, dgd_avg_stride, &dgd1[0], &dgd1[1],
+                       &dgd1[2], &dgd1[3], &dgd1[4], &dgd1[5], &dgd1[6]);
+
+          // Compute all elements from the combination of both columns (49
+          // elements).
+          compute_H_two_rows_win7(dgd0, dgd1, col0, col1, H_tmp);
+        }
+      }
+    }
+    dgd_avg += downsample_factor * dgd_avg_stride;
+    src_avg += src_avg_stride;
+  } while (--height != 0);
+
+  // Transpose M_trn.
+  acc_transpose_M(M, M_trn, 7, downsample_factor);
+
+  // Copy upper triangle of H in the lower one.
+  copy_upper_triangle(H, H_tmp, wiener_win2, downsample_factor);
+}
+
+// This function computes two matrices: the cross-correlation between the src
+// buffer and dgd buffer (M), and the auto-covariance of the dgd buffer (H).
+//
+// 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
+// accesses, as it means we do contiguous loads of dgd but strided stores to M.
+// As a result, we use an intermediate matrix M_trn which is instead filled
+// such that one row of the wiener window gives one row of M_trn. Once fully
+// computed, M_trn is then transposed to return M.
+//
+// H is of size 25 * 25. It is filled by multiplying every pair of elements of
+// the wiener window together. Since it is a symmetric matrix, we only compute
+// the upper triangle, and then copy it down to the lower one. Here we fill it
+// by taking each different pair of columns, and multiplying all the elements of
+// the first one with all the elements of the second one, with a special case
+// when multiplying a column by itself.
+static INLINE void compute_stats_win5_sve(int16_t *dgd_avg, int dgd_avg_stride,
+                                          int16_t *src_avg, int src_avg_stride,
+                                          int width, int height, int64_t *M,
+                                          int64_t *H, int downsample_factor) {
+  const int wiener_win = 5;
+  const int wiener_win2 = wiener_win * wiener_win;
+
+  // Use a predicate to compute the last columns of the block for H.
+  svbool_t pattern = svwhilelt_b16_u32(0, width % 8);
+
+  // Use intermediate matrices for H and M to perform the computation, they
+  // will be accumulated into the original H and M at the end.
+  int64_t M_trn[25];
+  memset(M_trn, 0, sizeof(M_trn));
+
+  int64_t H_tmp[25 * 25];
+  memset(H_tmp, 0, sizeof(H_tmp));
+
+  do {
+    // Cross-correlation (M).
+    for (int row = 0; row < wiener_win; row++) {
+      int j = 0;
+      while (j < width) {
+        int16x8_t dgd[5];
+        load_s16_8x5(dgd_avg + row * dgd_avg_stride + j, 1, &dgd[0], &dgd[1],
+                     &dgd[2], &dgd[3], &dgd[4]);
+        int16x8_t s = vld1q_s16(src_avg + j);
+
+        // Compute all the elements of one row of M.
+        compute_M_one_row_win5(s, dgd, M_trn, row);
+
+        j += 8;
+      }
+    }
+
+    // Auto-covariance (H).
+    int j = 0;
+    while (j <= width - 8) {
+      for (int col0 = 0; col0 < wiener_win; col0++) {
+        // Load first column.
+        int16x8_t dgd0[5];
+        load_s16_8x5(dgd_avg + j + col0, dgd_avg_stride, &dgd0[0], &dgd0[1],
+                     &dgd0[2], &dgd0[3], &dgd0[4]);
+
+        // Perform computation of the first column with itself (15 elements).
+        // For the first column this will fill the upper triangle of the 5x5
+        // matrix at the top left of the H matrix. For the next columns this
+        // will fill the upper triangle of the other 5x5 matrices around H's
+        // diagonal.
+        compute_H_one_col(dgd0, col0, H_tmp, wiener_win, wiener_win2);
+
+        // All computation next to the matrix diagonal has already been done.
+        for (int col1 = col0 + 1; col1 < wiener_win; col1++) {
+          // Load second column and scale based on downsampling factor.
+          int16x8_t dgd1[5];
+          load_s16_8x5(dgd_avg + j + col1, dgd_avg_stride, &dgd1[0], &dgd1[1],
+                       &dgd1[2], &dgd1[3], &dgd1[4]);
+
+          // Compute all elements from the combination of both columns (25
+          // elements).
+          compute_H_two_rows_win5(dgd0, dgd1, col0, col1, H_tmp);
+        }
+      }
+      j += 8;
+    }
+
+    // Process remaining columns using a predicate to discard excess elements.
+    if (j < width) {
+      for (int col0 = 0; col0 < wiener_win; col0++) {
+        int16x8_t dgd0[5];
+        dgd0[0] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 0 * dgd_avg_stride + j + col0));
+        dgd0[1] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 1 * dgd_avg_stride + j + col0));
+        dgd0[2] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 2 * dgd_avg_stride + j + col0));
+        dgd0[3] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 3 * dgd_avg_stride + j + col0));
+        dgd0[4] = svget_neonq_s16(
+            svld1_s16(pattern, dgd_avg + 4 * dgd_avg_stride + j + col0));
+
+        // Perform computation of the first column with itself (15 elements).
+        // For the first column this will fill the upper triangle of the 5x5
+        // matrix at the top left of the H matrix. For the next columns this
+        // will fill the upper triangle of the other 5x5 matrices around H's
+        // diagonal.
+        compute_H_one_col(dgd0, col0, H_tmp, wiener_win, wiener_win2);
+
+        // All computation next to the matrix diagonal has already been done.
+        for (int col1 = col0 + 1; col1 < wiener_win; col1++) {
+          // Load second column and scale based on downsampling factor.
+          int16x8_t dgd1[5];
+          load_s16_8x5(dgd_avg + j + col1, dgd_avg_stride, &dgd1[0], &dgd1[1],
+                       &dgd1[2], &dgd1[3], &dgd1[4]);
+
+          // Compute all elements from the combination of both columns (25
+          // elements).
+          compute_H_two_rows_win5(dgd0, dgd1, col0, col1, H_tmp);
+        }
+      }
+    }
+    dgd_avg += downsample_factor * dgd_avg_stride;
+    src_avg += src_avg_stride;
+  } while (--height != 0);
+
+  // Transpose M_trn.
+  acc_transpose_M(M, M_trn, 5, downsample_factor);
+
+  // Copy upper triangle of H in the lower one.
+  copy_upper_triangle(H, H_tmp, wiener_win2, downsample_factor);
+}
+
+void av1_compute_stats_sve(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);
+
+  const int wiener_win2 = wiener_win * wiener_win;
+  const int wiener_halfwin = wiener_win >> 1;
+  const int32_t width = h_end - h_start;
+  const int32_t height = v_end - v_start;
+  const uint8_t *dgd_start = &dgd[v_start * dgd_stride + h_start];
+  memset(H, 0, sizeof(*H) * wiener_win2 * wiener_win2);
+  memset(M, 0, sizeof(*M) * wiener_win * wiener_win);
+
+  const uint8_t avg = find_average_sve(dgd_start, dgd_stride, width, height);
+  const int downsample_factor =
+      use_downsampled_wiener_stats ? WIENER_STATS_DOWNSAMPLE_FACTOR : 1;
+
+  // dgd_avg and src_avg have been memset to zero before calling this
+  // function, so round up the stride to the next multiple of 8 so that we
+  // don't have to worry about a tail loop when computing M.
+  const int dgd_avg_stride = ((width + 2 * wiener_halfwin) & ~7) + 8;
+  const int src_avg_stride = (width & ~7) + 8;
+
+  // Compute (dgd - avg) and store it in dgd_avg.
+  // 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;
+  compute_sub_avg(dgd_win, dgd_stride, avg, dgd_avg, dgd_avg_stride,
+                  width + 2 * wiener_halfwin, height + 2 * wiener_halfwin, 1);
+
+  // Compute (src - avg), downsample if necessary and store in src-avg.
+  const uint8_t *src_start = src + h_start + v_start * src_stride;
+  compute_sub_avg(src_start, src_stride * downsample_factor, avg, src_avg,
+                  src_avg_stride, width, height, downsample_factor);
+
+  const int downsample_height = height / downsample_factor;
+
+  // 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.
+  const int downsample_remainder = height % downsample_factor;
+
+  if (wiener_win == WIENER_WIN) {
+    compute_stats_win7_sve(dgd_avg, dgd_avg_stride, src_avg, src_avg_stride,
+                           width, downsample_height, M, H, downsample_factor);
+  } else {
+    compute_stats_win5_sve(dgd_avg, dgd_avg_stride, src_avg, src_avg_stride,
+                           width, downsample_height, M, H, downsample_factor);
+  }
+
+  if (downsample_remainder > 0) {
+    const int remainder_offset = height - downsample_remainder;
+    if (wiener_win == WIENER_WIN) {
+      compute_stats_win7_sve(
+          dgd_avg + remainder_offset * dgd_avg_stride, dgd_avg_stride,
+          src_avg + downsample_height * src_avg_stride, src_avg_stride, width,
+          1, M, H, downsample_remainder);
+    } else {
+      compute_stats_win5_sve(
+          dgd_avg + remainder_offset * dgd_avg_stride, dgd_avg_stride,
+          src_avg + downsample_height * src_avg_stride, src_avg_stride, width,
+          1, M, H, downsample_remainder);
+    }
+  }
+}