avfilter: add nnedi filter

Port of nnedi3 vapoursynth filter.
Signed-off-by: Paul B Mahol <onemda@gmail.com>

Changelog

@@ -63,6 +63,7 @@ version <next>:
 - Cineform HD decoder
 - new DCA decoder with full support for DTS-HD extensions
 - significant performance improvements in Windows Television (WTV) demuxer
+- nnedi deinterlacer
 
 
 version 2.8:

configure

@@ -2873,6 +2873,7 @@ mpdecimate_filter_deps="gpl"
 mpdecimate_filter_select="pixelutils"
 mptestsrc_filter_deps="gpl"
 negate_filter_deps="lut_filter"
+nnedi_filter_deps="gpl"
 ocr_filter_deps="libtesseract"
 ocv_filter_deps="libopencv"
 owdenoise_filter_deps="gpl"

doc/filters.texi

@@ -8490,6 +8490,115 @@ Negate input video.
 It accepts an integer in input; if non-zero it negates the
 alpha component (if available). The default value in input is 0.
 
+@section nnedi
+
+Deinterlace video using neural network edge directed interpolation.
+
+This filter accepts the following options:
+
+@table @option
+@item weights
+Mandatory option, without binary file filter can not work.
+Currently file can be found here:
+https://github.com/dubhater/vapoursynth-nnedi3/blob/master/src/nnedi3_weights.bin
+
+@item deint
+Set which frames to deinterlace, by default it is @code{all}.
+Can be @code{all} or @code{interlaced}.
+
+@item field
+Set mode of operation.
+
+Can be one of the following:
+
+@table @samp
+@item af
+Use frame flags, both fields.
+@item a
+Use frame flags, single field.
+@item t
+Use top field only.
+@item b
+Use bottom field only.
+@item ft
+Use both fields, top first.
+@item fb
+Use both fields, bottom first.
+@end table
+
+@item planes
+Set which planes to process, by default filter process all frames.
+
+@item nsize
+Set size of local neighborhood around each pixel, used by the predictor neural
+network.
+
+Can be one of the following:
+
+@table @samp
+@item s8x6
+@item s16x6
+@item s32x6
+@item s48x6
+@item s8x4
+@item s16x4
+@item s32x4
+@end table
+
+@item nns
+Set the number of neurons in predicctor neural network.
+Can be one of the following:
+
+@table @samp
+@item n16
+@item n32
+@item n64
+@item n128
+@item n256
+@end table
+
+@item qual
+Controls the number of different neural network predictions that are blended
+together to compute the final output value. Can be @code{fast}, default or
+@code{slow}.
+
+@item etype
+Set which set of weights to use in the predictor.
+Can be one of the following:
+
+@table @samp
+@item a
+weights trained to minimize absolute error
+@item s
+weights trained to minimize squared error
+@end table
+
+@item pscrn
+Controls whether or not the prescreener neural network is used to decide
+which pixels should be processed by the predictor neural network and which
+can be handled by simple cubic interpolation.
+The prescreener is trained to know whether cubic interpolation will be
+sufficient for a pixel or whether it should be predicted by the predictor nn.
+The computational complexity of the prescreener nn is much less than that of
+the predictor nn. Since most pixels can be handled by cubic interpolation,
+using the prescreener generally results in much faster processing.
+The prescreener is pretty accurate, so the difference between using it and not
+using it is almost always unnoticeable.
+
+Can be one of the following:
+
+@table @samp
+@item none
+@item original
+@item new
+@end table
+
+Default is @code{new}.
+
+@item fapprox
+Set various debugging flags.
+@end table
+
 @section noformat
 
 Force libavfilter not to use any of the specified pixel formats for the

libavfilter/Makefile

@@ -187,6 +187,7 @@ OBJS-$(CONFIG_MCDEINT_FILTER)                += vf_mcdeint.o
 OBJS-$(CONFIG_MERGEPLANES_FILTER)            += vf_mergeplanes.o framesync.o
 OBJS-$(CONFIG_MPDECIMATE_FILTER)             += vf_mpdecimate.o
 OBJS-$(CONFIG_NEGATE_FILTER)                 += vf_lut.o
+OBJS-$(CONFIG_NNEDI_FILTER)                  += vf_nnedi.o
 OBJS-$(CONFIG_NOFORMAT_FILTER)               += vf_format.o
 OBJS-$(CONFIG_NOISE_FILTER)                  += vf_noise.o
 OBJS-$(CONFIG_NULL_FILTER)                   += vf_null.o

libavfilter/allfilters.c

@@ -208,6 +208,7 @@ void avfilter_register_all(void)
     REGISTER_FILTER(MERGEPLANES,    mergeplanes,    vf);
     REGISTER_FILTER(MPDECIMATE,     mpdecimate,     vf);
     REGISTER_FILTER(NEGATE,         negate,         vf);
+    REGISTER_FILTER(NNEDI,          nnedi,          vf);
     REGISTER_FILTER(NOFORMAT,       noformat,       vf);
     REGISTER_FILTER(NOISE,          noise,          vf);
     REGISTER_FILTER(NULL,           null,           vf);

libavfilter/version.h

@@ -30,7 +30,7 @@
 #include "libavutil/version.h"
 
 #define LIBAVFILTER_VERSION_MAJOR   6
-#define LIBAVFILTER_VERSION_MINOR  27
+#define LIBAVFILTER_VERSION_MINOR  28
 #define LIBAVFILTER_VERSION_MICRO 100
 
 #define LIBAVFILTER_VERSION_INT AV_VERSION_INT(LIBAVFILTER_VERSION_MAJOR, \

libavfilter/vf_nnedi.c

+/*
+ * Copyright (C) 2010-2011 Kevin Stone
+ * Copyright (C) 2016 Paul B Mahol
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 2 of the License, or
+ * (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License along
+ * with FFmpeg; if not, write to the Free Software Foundation, Inc.,
+ * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+ */
+
+#include <float.h>
+
+#include "libavutil/common.h"
+#include "libavutil/float_dsp.h"
+#include "libavutil/imgutils.h"
+#include "libavutil/opt.h"
+#include "libavutil/pixdesc.h"
+#include "avfilter.h"
+#include "formats.h"
+#include "internal.h"
+#include "video.h"
+
+typedef struct FrameData {
+    uint8_t *paddedp[3];
+    int padded_stride[3];
+    int padded_width[3];
+    int padded_height[3];
+
+    uint8_t *dstp[3];
+    int dst_stride[3];
+
+    int field[3];
+
+    int32_t *lcount[3];
+    float *input;
+    float *temp;
+} FrameData;
+
+typedef struct NNEDIContext {
+    const AVClass *class;
+
+    char *weights_file;
+
+    AVFrame *src;
+    AVFrame *second;
+    AVFrame *dst;
+    int eof;
+    int64_t cur_pts;
+
+    AVFloatDSPContext *fdsp;
+    int nb_planes;
+    int linesize[4];
+    int planeheight[4];
+
+    float *weights0;
+    float *weights1[2];
+    int asize;
+    int nns;
+    int xdia;
+    int ydia;
+
+    // Parameters
+    int deint;
+    int field;
+    int process_plane;
+    int nsize;
+    int nnsparam;
+    int qual;
+    int etype;
+    int pscrn;
+    int fapprox;
+
+    int max_value;
+
+    void (*copy_pad)(const AVFrame *, FrameData *, struct NNEDIContext *, int);
+    void (*evalfunc_0)(struct NNEDIContext *, FrameData *);
+    void (*evalfunc_1)(struct NNEDIContext *, FrameData *);
+
+    // Functions used in evalfunc_0
+    void (*readpixels)(const uint8_t *, const int, float *);
+    void (*compute_network0)(struct NNEDIContext *s, const float *, const float *, uint8_t *);
+    int32_t (*process_line0)(const uint8_t *, int, uint8_t *, const uint8_t *, const int, const int, const int);
+
+    // Functions used in evalfunc_1
+    void (*extract)(const uint8_t *, const int, const int, const int, float *, float *);
+    void (*dot_prod)(struct NNEDIContext *, const float *, const float *, float *, const int, const int, const float *);
+    void (*expfunc)(float *, const int);
+    void (*wae5)(const float *, const int, float *);
+
+    FrameData frame_data;
+} NNEDIContext;
+
+#define OFFSET(x) offsetof(NNEDIContext, x)
+#define FLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM
+
+static const AVOption nnedi_options[] = {
+    {"weights",  "set weights file", OFFSET(weights_file),  AV_OPT_TYPE_STRING, {.str="nnedi3_weights.bin"}, 0, 0, FLAGS },
+    {"deint",         "set which frames to deinterlace", OFFSET(deint),         AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "deint" },
+        {"all",        "deinterlace all frames",                       0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "deint" },
+        {"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "deint" },
+    {"field",  "set mode of operation", OFFSET(field),         AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, FLAGS, "field" },
+        {"af", "use frame flags, both fields",  0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, FLAGS, "field" },
+        {"a",  "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, FLAGS, "field" },
+        {"t",  "use top field only",            0, AV_OPT_TYPE_CONST, {.i64=0},  0, 0, FLAGS, "field" },
+        {"b",  "use bottom field only",         0, AV_OPT_TYPE_CONST, {.i64=1},  0, 0, FLAGS, "field" },
+        {"tf", "use both fields, top first",    0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "field" },
+        {"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "field" },
+    {"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 7, FLAGS },
+    {"nsize",  "set size of local neighborhood around each pixel, used by the predictor neural network", OFFSET(nsize), AV_OPT_TYPE_INT, {.i64=6}, 0, 6, FLAGS, "nsize" },
+        {"s8x6",     NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nsize" },
+        {"s16x6",    NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nsize" },
+        {"s32x6",    NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nsize" },
+        {"s48x6",    NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nsize" },
+        {"s8x4",     NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nsize" },
+        {"s16x4",    NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, FLAGS, "nsize" },
+        {"s32x4",    NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, FLAGS, "nsize" },
+    {"nns",    "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, FLAGS, "nns" },
+        {"n16",       NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nns" },
+        {"n32",       NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nns" },
+        {"n64",       NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nns" },
+        {"n128",      NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nns" },
+        {"n256",      NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nns" },
+    {"qual",  "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, FLAGS, "qual" },
+        {"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "qual" },
+        {"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "qual" },
+    {"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "etype" },
+        {"a",  "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "etype" },
+        {"s",  "weights trained to minimize squared error",  0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "etype" },
+    {"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 2, FLAGS, "pscrn" },
+        {"none",      NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "pscrn" },
+        {"original",  NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "pscrn" },
+        {"new",       NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "pscrn" },
+    {"fapprox",       NULL, OFFSET(fapprox),       AV_OPT_TYPE_INT, {.i64=0}, 0, 3, FLAGS },
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(nnedi);
+
+static int config_input(AVFilterLink *inlink)
+{
+    AVFilterContext *ctx = inlink->dst;
+    NNEDIContext *s = ctx->priv;
+    const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
+    int ret;
+
+    s->nb_planes = av_pix_fmt_count_planes(inlink->format);
+    if ((ret = av_image_fill_linesizes(s->linesize, inlink->format, inlink->w)) < 0)
+        return ret;
+
+    s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
+    s->planeheight[0] = s->planeheight[3] = inlink->h;
+
+    return 0;
+}
+
+static int config_output(AVFilterLink *outlink)
+{
+    AVFilterContext *ctx = outlink->src;
+    NNEDIContext *s = ctx->priv;
+
+    outlink->time_base.num = ctx->inputs[0]->time_base.num;
+    outlink->time_base.den = ctx->inputs[0]->time_base.den * 2;
+    outlink->w             = ctx->inputs[0]->w;
+    outlink->h             = ctx->inputs[0]->h;
+
+    if (s->field > 1 || s->field == -2)
+        outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate,
+                                       (AVRational){2, 1});
+
+    return 0;
+}
+
+static int query_formats(AVFilterContext *ctx)
+{
+    static const enum AVPixelFormat pix_fmts[] = {
+        AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
+        AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
+        AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
+        AV_PIX_FMT_YUVJ444P, AV_PIX_FMT_YUVJ440P,
+        AV_PIX_FMT_YUVJ422P, AV_PIX_FMT_YUVJ420P,
+        AV_PIX_FMT_YUVJ411P,
+        AV_PIX_FMT_GBRP,
+        AV_PIX_FMT_GRAY8,
+        AV_PIX_FMT_NONE
+    };
+
+    AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
+    if (!fmts_list)
+        return AVERROR(ENOMEM);
+    return ff_set_common_formats(ctx, fmts_list);
+}
+
+static void copy_pad(const AVFrame *src, FrameData *frame_data, NNEDIContext *s, int fn)
+{
+    const int off = 1 - fn;
+    int plane, y, x;
+
+    for (plane = 0; plane < s->nb_planes; plane++) {
+        const uint8_t *srcp = (const uint8_t *)src->data[plane];
+        uint8_t *dstp = (uint8_t *)frame_data->paddedp[plane];
+
+        const int src_stride = src->linesize[plane];
+        const int dst_stride = frame_data->padded_stride[plane];
+
+        const int src_height = s->planeheight[plane];
+        const int dst_height = frame_data->padded_height[plane];
+
+        const int src_width = s->linesize[plane];
+        const int dst_width = frame_data->padded_width[plane];
+
+        int c = 4;
+
+        if (!(s->process_plane & (1 << plane)))
+            continue;
+
+        // Copy.
+        for (y = off; y < src_height; y += 2)
+            memcpy(dstp + 32 + (6 + y) * dst_stride,
+                   srcp + y * src_stride,
+                   src_width * sizeof(uint8_t));
+
+        // And pad.
+        dstp += (6 + off) * dst_stride;
+        for (y = 6 + off; y < dst_height - 6; y += 2) {
+            int c = 2;
+
+            for (x = 0; x < 32; x++)
+                dstp[x] = dstp[64 - x];
+
+            for (x = dst_width - 32; x < dst_width; x++, c += 2)
+                dstp[x] = dstp[x - c];
+
+            dstp += dst_stride * 2;
+        }
+
+        dstp = (uint8_t *)frame_data->paddedp[plane];
+        for (y = off; y < 6; y += 2)
+            memcpy(dstp + y * dst_stride,
+                   dstp + (12 + 2 * off - y) * dst_stride,
+                   dst_width * sizeof(uint8_t));
+
+        for (y = dst_height - 6 + off; y < dst_height; y += 2, c += 4)
+            memcpy(dstp + y * dst_stride,
+                   dstp + (y - c) * dst_stride,
+                   dst_width * sizeof(uint8_t));
+    }
+}
+
+static void elliott(float *data, const int n)
+{
+    int i;
+
+    for (i = 0; i < n; i++)
+        data[i] = data[i] / (1.0f + FFABS(data[i]));
+}
+
+static void dot_prod(NNEDIContext *s, const float *data, const float *weights, float *vals, const int n, const int len, const float *scale)
+{
+    int i;
+
+    for (i = 0; i < n; i++) {
+        float sum;
+
+        sum = s->fdsp->scalarproduct_float(data, &weights[i * len], len);
+
+        vals[i] = sum * scale[0] + weights[n * len + i];
+    }
+}
+
+static void dot_prods(NNEDIContext *s, const float *dataf, const float *weightsf, float *vals, const int n, const int len, const float *scale)
+{
+    const int16_t *data = (int16_t *)dataf;
+    const int16_t *weights = (int16_t *)weightsf;
+    const float *wf = (float *)&weights[n * len];
+    int i, j;
+
+    for (i = 0; i < n; i++) {
+        int sum = 0, off = ((i >> 2) << 3) + (i & 3);
+        for (j = 0; j < len; j++)
+            sum += data[j] * weights[i * len + j];
+
+        vals[i] = sum * wf[off] * scale[0] + wf[off + 4];
+    }
+}
+
+static void compute_network0(NNEDIContext *s, const float *input, const float *weights, uint8_t *d)
+{
+    float t, temp[12], scale = 1.0f;
+
+    dot_prod(s, input, weights, temp, 4, 48, &scale);
+    t = temp[0];
+    elliott(temp, 4);
+    temp[0] = t;
+    dot_prod(s, temp, weights + 4 * 49, temp + 4, 4, 4, &scale);
+    elliott(temp + 4, 4);
+    dot_prod(s, temp, weights + 4 * 49 + 4 * 5, temp + 8, 4, 8, &scale);
+    if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
+        d[0] = 1;
+    else
+        d[0] = 0;
+}
+
+static void compute_network0_i16(NNEDIContext *s, const float *inputf, const float *weightsf, uint8_t *d)
+{
+    const float *wf = weightsf + 2 * 48;
+    float t, temp[12], scale = 1.0f;
+
+    dot_prods(s, inputf, weightsf, temp, 4, 48, &scale);
+    t = temp[0];
+    elliott(temp, 4);
+    temp[0] = t;
+    dot_prod(s, temp, wf + 8, temp + 4, 4, 4, &scale);
+    elliott(temp + 4, 4);
+    dot_prod(s, temp, wf + 8 + 4 * 5, temp + 8, 4, 8, &scale);
+    if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
+        d[0] = 1;
+    else
+        d[0] = 0;
+}
+
+static void pixel2float48(const uint8_t *t8, const int pitch, float *p)
+{
+    const uint8_t *t = (const uint8_t *)t8;
+    int y, x;
+
+    for (y = 0; y < 4; y++)
+        for (x = 0; x < 12; x++)
+            p[y * 12 + x] = t[y * pitch * 2 + x];
+}
+
+static void byte2word48(const uint8_t *t, const int pitch, float *pf)
+{
+    int16_t *p = (int16_t *)pf;
+    int y, x;
+
+    for (y = 0; y < 4; y++)
+        for (x = 0; x < 12; x++)
+            p[y * 12 + x] = t[y * pitch * 2 + x];
+}
+
+static int32_t process_line0(const uint8_t *tempu, int width, uint8_t *dstp8, const uint8_t *src3p8, const int src_pitch, const int max_value, const int chroma)
+{
+    uint8_t *dstp = (uint8_t *)dstp8;
+    const uint8_t *src3p = (const uint8_t *)src3p8;
+    int minimum = 0;
+    int maximum = max_value - 1; // Technically the -1 is only needed for 8 and 16 bit input.
+    int count = 0, x;
+    for (x = 0; x < width; x++) {
+        if (tempu[x]) {
+            int tmp = 19 * (src3p[x + src_pitch * 2] + src3p[x + src_pitch * 4]) - 3 * (src3p[x] + src3p[x + src_pitch * 6]);
+            tmp /= 32;
+            dstp[x] = FFMAX(FFMIN(tmp, maximum), minimum);
+        } else {
+            memset(dstp + x, 255, sizeof(uint8_t));
+            count++;
+        }
+    }
+    return count;
+}
+
+// new prescreener functions
+static void byte2word64(const uint8_t *t, const int pitch, float *p)
+{
+    int16_t *ps = (int16_t *)p;
+    int y, x;
+
+    for (y = 0; y < 4; y++)
+        for (x = 0; x < 16; x++)
+            ps[y * 16 + x] = t[y * pitch * 2 + x];
+}
+
+static void compute_network0new(NNEDIContext *s, const float *datai, const float *weights, uint8_t *d)
+{
+    int16_t *data = (int16_t *)datai;
+    int16_t *ws = (int16_t *)weights;
+    float *wf = (float *)&ws[4 * 64];
+    float vals[8];
+    int mask, i, j;
+
+    for (i = 0; i < 4; i++) {
+        int sum = 0;
+        float t;
+
+        for (j = 0; j < 64; j++)
+            sum += data[j] * ws[(i << 3) + ((j >> 3) << 5) + (j & 7)];
+        t = sum * wf[i] + wf[4 + i];
+        vals[i] = t / (1.0f + FFABS(t));
+    }
+
+    for (i = 0; i < 4; i++) {
+        float sum = 0.0f;
+
+        for (j = 0; j < 4; j++)
+            sum += vals[j] * wf[8 + i + (j << 2)];
+        vals[4 + i] = sum + wf[8 + 16 + i];
+    }
+
+    mask = 0;
+    for (i = 0; i < 4; i++) {
+        if (vals[4 + i] > 0.0f)
+            mask |= (0x1 << (i << 3));
+    }
+
+    ((int *)d)[0] = mask;
+}
+
+static void evalfunc_0(NNEDIContext *s, FrameData *frame_data)
+{
+    float *input = frame_data->input;
+    const float *weights0 = s->weights0;
+    float *temp = frame_data->temp;
+    uint8_t *tempu = (uint8_t *)temp;
+    int plane, x, y;
+
+    // And now the actual work.
+    for (plane = 0; plane < s->nb_planes; plane++) {
+        const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane];
+        const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t);
+
+        const int width = frame_data->padded_width[plane];
+        const int height = frame_data->padded_height[plane];
+
+        uint8_t *dstp = (uint8_t *)frame_data->dstp[plane];
+        const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t);
+        const uint8_t *src3p;
+        int ystart, ystop;
+        int32_t *lcount;
+
+        if (!(s->process_plane & (1 << plane)))
+            continue;
+
+        for (y = 1 - frame_data->field[plane]; y < height - 12; y += 2) {
+            memcpy(dstp + y * dst_stride,
+                   srcp + 32 + (6 + y) * src_stride,
+                   (width - 64) * sizeof(uint8_t));
+
+        }
+
+        ystart = 6 + frame_data->field[plane];
+        ystop = height - 6;
+        srcp += ystart * src_stride;
+        dstp += (ystart - 6) * dst_stride - 32;
+        src3p = srcp - src_stride * 3;
+        lcount = frame_data->lcount[plane] - 6;
+
+        if (s->pscrn == 1) { // original
+            for (y = ystart; y < ystop; y += 2) {
+                for (x = 32; x < width - 32; x++) {
+                    s->readpixels((const uint8_t *)(src3p + x - 5), src_stride, input);
+                    s->compute_network0(s, input, weights0, tempu+x);
+                }
+                lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane);
+                src3p += src_stride * 2;
+                dstp += dst_stride * 2;
+            }
+        } else if (s->pscrn > 1) { // new
+            for (y = ystart; y < ystop; y += 2) {
+                for (x = 32; x < width - 32; x += 4) {
+                    s->readpixels((const uint8_t *)(src3p + x - 6), src_stride, input);
+                    s->compute_network0(s, input, weights0, tempu + x);
+                }
+                lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane);
+                src3p += src_stride * 2;
+                dstp += dst_stride * 2;
+            }
+        } else { // no prescreening
+            for (y = ystart; y < ystop; y += 2) {
+                memset(dstp + 32, 255, (width - 64) * sizeof(uint8_t));
+                lcount[y] += width - 64;
+                dstp += dst_stride * 2;
+            }
+        }
+    }
+}
+
+static void extract_m8(const uint8_t *srcp8, const int stride, const int xdia, const int ydia, float *mstd, float *input)
+{
+    // uint8_t or uint16_t or float
+    const uint8_t *srcp = (const uint8_t *)srcp8;
+
+    // int32_t or int64_t or double
+    int64_t sum = 0, sumsq = 0;
+    int y, x;
+
+    for (y = 0; y < ydia; y++) {
+        const uint8_t *srcpT = srcp + y * stride * 2;
+
+        for (x = 0; x < xdia; x++) {
+            sum += srcpT[x];
+            sumsq += (uint32_t)srcpT[x] * (uint32_t)srcpT[x];
+            input[x] = srcpT[x];
+        }
+        input += xdia;
+    }
+    const float scale = 1.0f / (xdia * ydia);
+    mstd[0] = sum * scale;
+    const double tmp = (double)sumsq * scale - (double)mstd[0] * mstd[0];
+    mstd[3] = 0.0f;
+    if (tmp <= FLT_EPSILON)
+        mstd[1] = mstd[2] = 0.0f;
+    else {
+        mstd[1] = sqrt(tmp);
+        mstd[2] = 1.0f / mstd[1];
+    }
+}
+
+static void extract_m8_i16(const uint8_t *srcp, const int stride, const int xdia, const int ydia, float *mstd, float *inputf)
+{
+    int16_t *input = (int16_t *)inputf;
+    int sum = 0, sumsq = 0;
+    int y, x;
+
+    for (y = 0; y < ydia; y++) {
+        const uint8_t *srcpT = srcp + y * stride * 2;
+        for (x = 0; x < xdia; x++) {
+            sum += srcpT[x];
+            sumsq += srcpT[x] * srcpT[x];
+            input[x] = srcpT[x];
+        }
+        input += xdia;
+    }
+    const float scale = 1.0f / (float)(xdia * ydia);
+    mstd[0] = sum * scale;
+    mstd[1] = sumsq * scale - mstd[0] * mstd[0];
+    mstd[3] = 0.0f;
+    if (mstd[1] <= FLT_EPSILON)
+        mstd[1] = mstd[2] = 0.0f;
+    else {
+        mstd[1] = sqrt(mstd[1]);
+        mstd[2] = 1.0f / mstd[1];
+    }
+}
+
+
+static const float exp_lo = -80.0f;
+static const float exp_hi = +80.0f;
+
+static void e2_m16(float *s, const int n)
+{
+    int i;
+
+    for (i = 0; i < n; i++)
+        s[i] = exp(av_clipf(s[i], exp_lo, exp_hi));
+}
+
+const float min_weight_sum = 1e-10f;
+
+static void weighted_avg_elliott_mul5_m16(const float *w, const int n, float *mstd)
+{
+    float vsum = 0.0f, wsum = 0.0f;
+    int i;
+
+    for (i = 0; i < n; i++) {
+        vsum += w[i] * (w[n + i] / (1.0f + FFABS(w[n + i])));
+        wsum += w[i];
+    }
+    if (wsum > min_weight_sum)
+        mstd[3] += ((5.0f * vsum) / wsum) * mstd[1] + mstd[0];
+    else
+        mstd[3] += mstd[0];
+}
+
+
+static void evalfunc_1(NNEDIContext *s, FrameData *frame_data)
+{
+    float *input = frame_data->input;
+    float *temp = frame_data->temp;
+    float **weights1 = s->weights1;
+    const int qual = s->qual;
+    const int asize = s->asize;
+    const int nns = s->nns;
+    const int xdia = s->xdia;
+    const int xdiad2m1 = (xdia / 2) - 1;
+    const int ydia = s->ydia;
+    const float scale = 1.0f / (float)qual;
+    int plane, y, x, i;
+
+    for (plane = 0; plane < s->nb_planes; plane++) {
+        if (!(s->process_plane & (1 << plane)))
+            continue;
+
+        const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane];
+        const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t);
+
+        const int width = frame_data->padded_width[plane];
+        const int height = frame_data->padded_height[plane];
+
+        uint8_t *dstp = (uint8_t *)frame_data->dstp[plane];
+        const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t);
+
+        const int ystart = frame_data->field[plane];
+        const int ystop = height - 12;
+
+        srcp += (ystart + 6) * src_stride;
+        dstp += ystart * dst_stride - 32;
+        const uint8_t *srcpp = srcp - (ydia - 1) * src_stride - xdiad2m1;
+
+        for (y = ystart; y < ystop; y += 2) {
+            for (x = 32; x < width - 32; x++) {
+                uint32_t pixel = 0;
+                memcpy(&pixel, dstp + x, sizeof(uint8_t));
+
+                uint32_t all_ones = 0;
+                memset(&all_ones, 255, sizeof(uint8_t));
+
+                if (pixel != all_ones)
+                    continue;
+
+                float mstd[4];
+                s->extract((const uint8_t *)(srcpp + x), src_stride, xdia, ydia, mstd, input);
+                for (i = 0; i < qual; i++) {
+                    s->dot_prod(s, input, weights1[i], temp, nns * 2, asize, mstd + 2);
+                    s->expfunc(temp, nns);
+                    s->wae5(temp, nns, mstd);
+                }
+
+                dstp[x] = FFMIN(FFMAX((int)(mstd[3] * scale + 0.5f), 0), s->max_value);
+            }
+            srcpp += src_stride * 2;
+            dstp += dst_stride * 2;
+        }
+    }
+}
+
+#define NUM_NSIZE 7
+#define NUM_NNS 5
+
+static int roundds(const double f)
+{
+    if (f - floor(f) >= 0.5)
+        return FFMIN((int)ceil(f), 32767);
+    return FFMAX((int)floor(f), -32768);
+}
+
+static void select_functions(NNEDIContext *s)
+{
+    s->copy_pad = copy_pad;
+    s->evalfunc_0 = evalfunc_0;
+    s->evalfunc_1 = evalfunc_1;
+
+    // evalfunc_0
+    s->process_line0 = process_line0;
+
+    if (s->pscrn < 2) { // original prescreener
+        if (s->fapprox & 1) { // int16 dot products
+            s->readpixels = byte2word48;
+            s->compute_network0 = compute_network0_i16;
+        } else {
+            s->readpixels = pixel2float48;
+            s->compute_network0 = compute_network0;
+        }
+    } else { // new prescreener
+        // only int16 dot products
+        s->readpixels = byte2word64;
+        s->compute_network0 = compute_network0new;
+    }
+
+    // evalfunc_1
+    s->wae5 = weighted_avg_elliott_mul5_m16;
+
+    if (s->fapprox & 2) { // use int16 dot products
+        s->extract = extract_m8_i16;
+        s->dot_prod = dot_prods;
+    } else { // use float dot products
+        s->extract = extract_m8;
+        s->dot_prod = dot_prod;
+    }
+
+    s->expfunc = e2_m16;
+}
+
+static int modnpf(const int m, const int n)
+{
+    if ((m % n) == 0)
+        return m;
+    return m + n - (m % n);
+}
+
+static int get_frame(AVFilterContext *ctx, int is_second)
+{
+    NNEDIContext *s = ctx->priv;
+    AVFilterLink *outlink = ctx->outputs[0];
+    AVFrame *src = s->src;
+    FrameData *frame_data;
+    int effective_field = s->field;
+    size_t temp_size;
+    int field_n;
+    int plane;
+
+    if (effective_field > 1)
+        effective_field -= 2;
+    else if (effective_field < 0)
+        effective_field += 2;
+
+    if (s->field < 0 && src->interlaced_frame && src->top_field_first == 0)
+        effective_field = 0;
+    else if (s->field < 0 && src->interlaced_frame && src->top_field_first == 1)
+        effective_field = 1;
+    else
+        effective_field = !effective_field;
+
+    if (s->field > 1 || s->field == -2) {
+        if (is_second) {
+            field_n = (effective_field == 0);
+        } else {
+            field_n = (effective_field == 1);
+        }
+    } else {
+        field_n = effective_field;
+    }
+
+    s->dst = ff_get_video_buffer(outlink, outlink->w, outlink->h);
+    if (!s->dst)
+        return AVERROR(ENOMEM);
+    av_frame_copy_props(s->dst, src);
+    s->dst->interlaced_frame = 0;
+
+    frame_data = &s->frame_data;
+
+    for (plane = 0; plane < s->nb_planes; plane++) {
+        int dst_height = s->planeheight[plane];
+        int dst_width = s->linesize[plane];
+
+        const int min_alignment = 16;
+        const int min_pad = 10;
+
+        if (!(s->process_plane & (1 << plane))) {
+            av_image_copy_plane(s->dst->data[plane], s->dst->linesize[plane],
+                                src->data[plane], src->linesize[plane],
+                                s->linesize[plane],
+                                s->planeheight[plane]);
+            continue;
+        }
+
+        frame_data->padded_width[plane]  = dst_width + 64;
+        frame_data->padded_height[plane] = dst_height + 12;
+        frame_data->padded_stride[plane] = modnpf(frame_data->padded_width[plane] + min_pad, min_alignment); // TODO: maybe min_pad is in pixels too?
+        if (!frame_data->paddedp[plane]) {
+            frame_data->paddedp[plane] = av_malloc_array(frame_data->padded_stride[plane], frame_data->padded_height[plane]);
+            if (!frame_data->paddedp[plane])
+                return AVERROR(ENOMEM);
+        }
+
+        frame_data->dstp[plane] = s->dst->data[plane];
+        frame_data->dst_stride[plane] = s->dst->linesize[plane];
+
+        if (!frame_data->lcount[plane]) {
+            frame_data->lcount[plane] = av_calloc(dst_height, sizeof(int32_t) * 16);
+            if (!frame_data->lcount[plane])
+                return AVERROR(ENOMEM);
+        } else {
+            memset(frame_data->lcount[plane], 0, dst_height * sizeof(int32_t) * 16);
+        }
+
+        frame_data->field[plane] = field_n;
+    }
+
+    if (!frame_data->input) {
+        frame_data->input = av_malloc(512 * sizeof(float));
+        if (!frame_data->input)
+            return AVERROR(ENOMEM);
+    }
+    // evalfunc_0 requires at least padded_width[0] bytes.
+    // evalfunc_1 requires at least 512 floats.
+    if (!frame_data->temp) {
+        temp_size = FFMAX(frame_data->padded_width[0], 512 * sizeof(float));
+        frame_data->temp = av_malloc(temp_size);
+        if (!frame_data->temp)
+            return AVERROR(ENOMEM);
+    }
+
+    // Copy src to a padded "frame" in frame_data and mirror the edges.
+    s->copy_pad(src, frame_data, s, field_n);
+
+    // Handles prescreening and the cubic interpolation.
+    s->evalfunc_0(s, frame_data);
+
+    // The rest.
+    s->evalfunc_1(s, frame_data);
+
+    return 0;
+}
+
+static int filter_frame(AVFilterLink *inlink, AVFrame *src)
+{
+    AVFilterContext *ctx = inlink->dst;
+    AVFilterLink *outlink = ctx->outputs[0];
+    NNEDIContext *s = ctx->priv;
+    int ret;
+
+    if ((s->field > 1 ||
+         s->field == -2) && !s->second) {
+        goto second;
+    } else if (s->field > 1 ||
+               s->field == -2) {
+        AVFrame *dst;
+
+        s->src = s->second;
+        ret = get_frame(ctx, 1);
+        if (ret < 0) {
+            av_frame_free(&s->dst);
+            av_frame_free(&s->src);
+            av_frame_free(&s->second);
+            return ret;
+        }
+        dst = s->dst;
+
+        if (src->pts != AV_NOPTS_VALUE &&
+            dst->pts != AV_NOPTS_VALUE)
+            dst->pts += src->pts;
+        else
+            dst->pts = AV_NOPTS_VALUE;
+
+        ret = ff_filter_frame(outlink, dst);
+        if (ret < 0)
+            return ret;
+        if (s->eof)
+            return 0;
+        s->cur_pts = s->second->pts;
+        av_frame_free(&s->second);
+second:
+        if ((s->deint && src->interlaced_frame &&
+             !ctx->is_disabled) ||
+            (!s->deint && !ctx->is_disabled)) {
+            s->second = src;
+        }
+    }
+
+    if ((s->deint && !src->interlaced_frame) || ctx->is_disabled) {
+        AVFrame *dst = av_frame_clone(src);
+        if (!dst) {
+            av_frame_free(&src);
+            av_frame_free(&s->second);
+            return AVERROR(ENOMEM);
+        }
+
+        if (s->field > 1 || s->field == -2) {
+            av_frame_free(&s->second);
+            if ((s->deint && src->interlaced_frame) ||
+                (!s->deint))
+                s->second = src;
+        } else {
+            av_frame_free(&src);
+        }
+        if (dst->pts != AV_NOPTS_VALUE)
+            dst->pts *= 2;
+        return ff_filter_frame(outlink, dst);
+    }
+
+    s->src = src;
+    ret = get_frame(ctx, 0);
+    if (ret < 0) {
+        av_frame_free(&s->dst);
+        av_frame_free(&s->src);
+        av_frame_free(&s->second);
+        return ret;
+    }
+
+    if (src->pts != AV_NOPTS_VALUE)
+        s->dst->pts = src->pts * 2;
+    if (s->field <= 1 && s->field > -2) {
+        av_frame_free(&src);
+        s->src = NULL;
+    }
+
+    return ff_filter_frame(outlink, s->dst);
+}
+
+static int request_frame(AVFilterLink *link)
+{
+    AVFilterContext *ctx = link->src;
+    NNEDIContext *s = ctx->priv;
+    int ret;
+
+    if (s->eof)
+        return AVERROR_EOF;
+
+    ret  = ff_request_frame(ctx->inputs[0]);
+
+    if (ret == AVERROR_EOF && s->second) {
+        AVFrame *next = av_frame_clone(s->second);
+
+        if (!next)
+            return AVERROR(ENOMEM);
+
+        next->pts = s->second->pts * 2 - s->cur_pts;
+        s->eof = 1;
+
+        filter_frame(ctx->inputs[0], next);
+    } else if (ret < 0) {
+        return ret;
+    }
+
+    return 0;
+}
+
+static av_cold int init(AVFilterContext *ctx)
+{
+    NNEDIContext *s = ctx->priv;
+    FILE *weights_file = NULL;
+    int64_t expected_size = 13574928;
+    int64_t weights_size;
+    float *bdata;
+    size_t bytes_read;
+    const int xdia_table[NUM_NSIZE] = { 8, 16, 32, 48, 8, 16, 32 };
+    const int ydia_table[NUM_NSIZE] = { 6, 6, 6, 6, 4, 4, 4 };
+    const int nns_table[NUM_NNS] = { 16, 32, 64, 128, 256 };
+    const int dims0 = 49 * 4 + 5 * 4 + 9 * 4;
+    const int dims0new = 4 * 65 + 4 * 5;
+    const int dims1 = nns_table[s->nnsparam] * 2 * (xdia_table[s->nsize] * ydia_table[s->nsize] + 1);
+    int dims1tsize = 0;
+    int dims1offset = 0;
+    int ret = 0, i, j, k;
+
+    weights_file = fopen(s->weights_file, "rb");
+    if (!weights_file) {
+        av_log(ctx, AV_LOG_ERROR, "No weights file provided, aborting!\n");
+        return AVERROR(EINVAL);
+    }
+
+    if (fseek(weights_file, 0, SEEK_END)) {
+        av_log(ctx, AV_LOG_ERROR, "Couldn't seek to the end of weights file.\n");
+        fclose(weights_file);
+        return AVERROR(EINVAL);
+    }
+
+    weights_size = ftell(weights_file);
+
+    if (weights_size == -1) {
+        fclose(weights_file);
+        av_log(ctx, AV_LOG_ERROR, "Couldn't get size of weights file.\n");
+        return AVERROR(EINVAL);
+    } else if (weights_size != expected_size) {
+        fclose(weights_file);
+        av_log(ctx, AV_LOG_ERROR, "Unexpected weights file size.\n");
+        return AVERROR(EINVAL);
+    }
+
+    if (fseek(weights_file, 0, SEEK_SET)) {
+        fclose(weights_file);
+        av_log(ctx, AV_LOG_ERROR, "Couldn't seek to the start of weights file.\n");
+        return AVERROR(EINVAL);
+    }
+
+    bdata = (float *)av_malloc(expected_size);
+    if (!bdata) {
+        fclose(weights_file);
+        return AVERROR(ENOMEM);
+    }
+
+    bytes_read = fread(bdata, 1, expected_size, weights_file);
+
+    if (bytes_read != (size_t)expected_size) {
+        fclose(weights_file);
+        ret = AVERROR_INVALIDDATA;
+        av_log(ctx, AV_LOG_ERROR, "Couldn't read weights file.\n");
+        goto fail;
+    }
+
+    fclose(weights_file);
+
+    for (j = 0; j < NUM_NNS; j++) {
+        for (i = 0; i < NUM_NSIZE; i++) {
+            if (i == s->nsize && j == s->nnsparam)
+                dims1offset = dims1tsize;
+            dims1tsize += nns_table[j] * 2 * (xdia_table[i] * ydia_table[i] + 1) * 2;
+        }
+    }
+
+    s->weights0 = av_malloc_array(FFMAX(dims0, dims0new), sizeof(float));
+    if (!s->weights0) {
+        ret = AVERROR(ENOMEM);
+        goto fail;
+    }
+
+    for (i = 0; i < 2; i++) {
+        s->weights1[i] = av_malloc_array(dims1, sizeof(float));
+        if (!s->weights1[i]) {
+            ret = AVERROR(ENOMEM);
+            goto fail;
+        }
+    }
+
+    // Adjust prescreener weights
+    if (s->pscrn >= 2) {// using new prescreener
+        const float *bdw;
+        int16_t *ws;
+        float *wf;
+        double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
+        int *offt = av_calloc(4 * 64, sizeof(int));
+
+        if (!offt) {
+            ret = AVERROR(ENOMEM);
+            goto fail;
+        }
+
+        for (j = 0; j < 4; j++)
+            for (k = 0; k < 64; k++)
+                offt[j * 64 + k] = ((k >> 3) << 5) + ((j & 3) << 3) + (k & 7);
+
+        bdw = bdata + dims0 + dims0new * (s->pscrn - 2);
+        ws = (int16_t *)s->weights0;
+        wf = (float *)&ws[4 * 64];
+        // Calculate mean weight of each first layer neuron
+        for (j = 0; j < 4; j++) {
+            double cmean = 0.0;
+            for (k = 0; k < 64; k++)
+                cmean += bdw[offt[j * 64 + k]];
+            mean[j] = cmean / 64.0;
+        }
+        // Factor mean removal and 1.0/127.5 scaling
+        // into first layer weights. scale to int16 range
+        for (j = 0; j < 4; j++) {
+            double scale, mval = 0.0;
+
+            for (k = 0; k < 64; k++)
+                mval = FFMAX(mval, FFABS((bdw[offt[j * 64 + k]] - mean[j]) / 127.5));
+            scale = 32767.0 / mval;
+            for (k = 0; k < 64; k++)
+                ws[offt[j * 64 + k]] = roundds(((bdw[offt[j * 64 + k]] - mean[j]) / 127.5) * scale);
+            wf[j] = (float)(mval / 32767.0);
+        }
+        memcpy(wf + 4, bdw + 4 * 64, (dims0new - 4 * 64) * sizeof(float));
+        av_free(offt);
+    } else { // using old prescreener
+        double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
+        // Calculate mean weight of each first layer neuron
+        for (j = 0; j < 4; j++) {
+            double cmean = 0.0;
+            for (k = 0; k < 48; k++)
+                cmean += bdata[j * 48 + k];
+            mean[j] = cmean / 48.0;
+        }
+        if (s->fapprox & 1) {// use int16 dot products in first layer
+            int16_t *ws = (int16_t *)s->weights0;
+            float *wf = (float *)&ws[4 * 48];
+            // Factor mean removal and 1.0/127.5 scaling
+            // into first layer weights. scale to int16 range
+            for (j = 0; j < 4; j++) {
+                double mval = 0.0;
+                for (k = 0; k < 48; k++)
+                    mval = FFMAX(mval, FFABS((bdata[j * 48 + k] - mean[j]) / 127.5));
+                const double scale = 32767.0 / mval;
+                for (k = 0; k < 48; k++)
+                    ws[j * 48 + k] = roundds(((bdata[j * 48 + k] - mean[j]) / 127.5) * scale);
+                wf[j] = (float)(mval / 32767.0);
+            }
+            memcpy(wf + 4, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float));
+        } else {// use float dot products in first layer
+            double half = (1 << 8) - 1;
+
+            half /= 2;
+
+            // Factor mean removal and 1.0/half scaling
+            // into first layer weights.
+            for (j = 0; j < 4; j++)
+                for (k = 0; k < 48; k++)
+                    s->weights0[j * 48 + k] = (float)((bdata[j * 48 + k] - mean[j]) / half);
+            memcpy(s->weights0 + 4 * 48, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float));
+        }
+    }
+
+    // Adjust prediction weights
+    for (i = 0; i < 2; i++) {
+        const float *bdataT = bdata + dims0 + dims0new * 3 + dims1tsize * s->etype + dims1offset + i * dims1;
+        const int nnst = nns_table[s->nnsparam];
+        const int asize = xdia_table[s->nsize] * ydia_table[s->nsize];
+        const int boff = nnst * 2 * asize;
+        double *mean = (double *)av_calloc(asize + 1 + nnst * 2, sizeof(double));
+
+        if (!mean) {
+            ret = AVERROR(ENOMEM);
+            goto fail;
+        }
+
+        // Calculate mean weight of each neuron (ignore bias)
+        for (j = 0; j < nnst * 2; j++) {
+            double cmean = 0.0;
+            for (k = 0; k < asize; k++)
+                cmean += bdataT[j * asize + k];
+            mean[asize + 1 + j] = cmean / (double)asize;
+        }
+        // Calculate mean softmax neuron
+        for (j = 0; j < nnst; j++) {
+            for (k = 0; k < asize; k++)
+                mean[k] += bdataT[j * asize + k] - mean[asize + 1 + j];
+            mean[asize] += bdataT[boff + j];
+        }
+        for (j = 0; j < asize + 1; j++)
+            mean[j] /= (double)(nnst);
+
+        if (s->fapprox & 2) { // use int16 dot products
+            int16_t *ws = (int16_t *)s->weights1[i];
+            float *wf = (float *)&ws[nnst * 2 * asize];
+            // Factor mean removal into weights, remove global offset from
+            // softmax neurons, and scale weights to int16 range.
+            for (j = 0; j < nnst; j++) { // softmax neurons
+                double scale, mval = 0.0;
+                for (k = 0; k < asize; k++)
+                    mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]));
+                scale = 32767.0 / mval;
+                for (k = 0; k < asize; k++)
+                    ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]) * scale);
+                wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0);
+                wf[(j >> 2) * 8 + (j & 3) + 4] = (float)(bdataT[boff + j] - mean[asize]);
+            }
+            for (j = nnst; j < nnst * 2; j++) { // elliott neurons
+                double scale, mval = 0.0;
+                for (k = 0; k < asize; k++)
+                    mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j]));
+                scale = 32767.0 / mval;
+                for (k = 0; k < asize; k++)
+                    ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j]) * scale);
+                wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0);
+                wf[(j >> 2) * 8 + (j & 3) + 4] = bdataT[boff + j];
+            }
+        } else { // use float dot products
+            // Factor mean removal into weights, and remove global
+            // offset from softmax neurons.
+            for (j = 0; j < nnst * 2; j++) {
+                for (k = 0; k < asize; k++) {
+                    const double q = j < nnst ? mean[k] : 0.0;
+                    s->weights1[i][j * asize + k] = (float)(bdataT[j * asize + k] - mean[asize + 1 + j] - q);
+                }
+                s->weights1[i][boff + j] = (float)(bdataT[boff + j] - (j < nnst ? mean[asize] : 0.0));
+            }
+        }
+        av_free(mean);
+    }
+
+    s->nns = nns_table[s->nnsparam];
+    s->xdia = xdia_table[s->nsize];
+    s->ydia = ydia_table[s->nsize];
+    s->asize = xdia_table[s->nsize] * ydia_table[s->nsize];
+
+    s->max_value = 65535 >> 8;
+
+    select_functions(s);
+
+    s->fdsp = avpriv_float_dsp_alloc(0);
+    if (!s->fdsp)
+        return AVERROR(ENOMEM);
+
+fail:
+    av_free(bdata);
+    return ret;
+}
+
+static av_cold void uninit(AVFilterContext *ctx)
+{
+    NNEDIContext *s = ctx->priv;
+    int i;
+
+    av_freep(&s->weights0);
+
+    for (i = 0; i < 2; i++)
+        av_freep(&s->weights1[i]);
+
+    for (i = 0; i < s->nb_planes; i++) {
+        av_freep(&s->frame_data.paddedp[i]);
+        av_freep(&s->frame_data.lcount[i]);
+    }
+
+    av_freep(&s->frame_data.input);
+    av_freep(&s->frame_data.temp);
+    av_frame_free(&s->second);
+}
+
+static const AVFilterPad inputs[] = {
+    {
+        .name          = "default",
+        .type          = AVMEDIA_TYPE_VIDEO,
+        .filter_frame  = filter_frame,
+        .config_props  = config_input,
+    },
+    { NULL }
+};
+
+static const AVFilterPad outputs[] = {
+    {
+        .name          = "default",
+        .type          = AVMEDIA_TYPE_VIDEO,
+        .config_props  = config_output,
+        .request_frame = request_frame,
+    },
+    { NULL }
+};
+
+AVFilter ff_vf_nnedi = {
+    .name          = "nnedi",
+    .description   = NULL_IF_CONFIG_SMALL("Apply neural network edge directed interpolation intra-only deinterlacer."),
+    .priv_size     = sizeof(NNEDIContext),
+    .priv_class    = &nnedi_class,
+    .init          = init,
+    .uninit        = uninit,
+    .query_formats = query_formats,
+    .inputs        = inputs,
+    .outputs       = outputs,
+    .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL,
+};