/*

 *  prediction.c:	Range image prediction with MC or ND	

 *

 *  Written by:		Ullrich Hafner

 *			Michael Unger

 *		

 *  This file is part of FIASCO (Fractal Image And Sequence COdec)

 *  Copyright (C) 1994-2000 Ullrich Hafner

 */

/*

 *  $Date: 2000/06/14 20:50:51 $

 *  $Author: hafner $

 *  $Revision: 5.1 $

 *  $State: Exp $

 */

#include "config.h"

#include <string.h>

#include "types.h"

#include "macros.h"

#include "error.h"

#include "cwfa.h"

#include "ip.h"

#include "control.h"

#include "misc.h"

#include "subdivide.h"

#include "bintree.h"

#include "domain-pool.h"

#include "approx.h"

#include "wfalib.h"

#include "mwfa.h"

#include "prediction.h"

#include "decoder.h"

/*****************************************************************************

			     local variables

*****************************************************************************/

typedef struct state_data

{

   real_t final_distribution;

   byte_t level_of_state;

   byte_t domain_type;

   real_t *images_of_state;

   real_t *inner_products;

   real_t *ip_states_state [MAXLEVEL];

   word_t tree [MAXLABELS];

   mv_t	  mv_tree [MAXLABELS];

   word_t y_state [MAXLABELS];

   byte_t y_column [MAXLABELS];

   byte_t prediction [MAXLABELS];

   u_word_t x [MAXLABELS];

   u_word_t y [MAXLABELS];

   real_t weight [MAXLABELS][MAXEDGES + 1];

   word_t int_weight [MAXLABELS][MAXEDGES + 1];

   word_t into [MAXLABELS][MAXEDGES + 1];

} state_data_t;

/*****************************************************************************

				prototypes

*****************************************************************************/

static real_t

nd_prediction (real_t max_costs, real_t price, unsigned band, int y_state,

	       range_t *range, wfa_t *wfa, coding_t *c);

static real_t

mc_prediction (real_t max_costs, real_t price, unsigned band, int y_state,

	       range_t *range, wfa_t *wfa, coding_t *c);

static state_data_t *

store_state_data (unsigned from, unsigned to, unsigned max_level,

		  wfa_t *wfa, coding_t *c);

static void

restore_state_data (unsigned from, unsigned to, unsigned max_level,

		    state_data_t *data, wfa_t *wfa, coding_t *c);

/*****************************************************************************

				public code

*****************************************************************************/

real_t

predict_range (real_t max_costs, real_t price, range_t *range, wfa_t *wfa,

	       coding_t *c, unsigned band, int y_state, unsigned states,

	       const tree_t *tree_model, const tree_t *p_tree_model,

	       const void *domain_model, const void *d_domain_model,

	       const void *coeff_model, const void *d_coeff_model)

{

   unsigned	 state;		     	/* counter */

   void		*rec_domain_model;   	/* domain model after recursion */

   void		*rec_d_domain_model; 	/* p domain model after recursion */

   void		*rec_coeff_model;    	/* coeff model after recursion */

   void		*rec_d_coeff_model;  	/* d coeff model after recursion */

   tree_t	 rec_tree_model;	/* tree_model after '' */

   tree_t	 rec_p_tree_model;    	/* p_tree_model after '' */

   unsigned	 rec_states;	     	/* wfa->states after '' */

   real_t	*rec_pixels;	     	/* c->pixels after '' */

   state_data_t	*rec_state_data;     	/* state_data struct after '' */

   real_t	 costs;		     	/* current approximation costs */

   unsigned	 level;		     	/* counter */

   state_data_t	*sd;		     	/* pointer to state_data field */

   /*

    *  Store WFA data from state 'lc_states' to 'wfa->states' - 1 and

    *  current state of probability models.

    */

   rec_domain_model   = c->domain_pool->model;

   rec_d_domain_model = c->d_domain_pool->model;

   rec_coeff_model    = c->coeff->model;

   rec_d_coeff_model  = c->d_coeff->model;

   rec_tree_model     = c->tree;

   rec_p_tree_model   = c->p_tree;

   rec_states         = wfa->states;	

   rec_pixels         = c->pixels;

   rec_state_data     = store_state_data (states, rec_states - 1,

					  c->options.lc_max_level, wfa, c);

   /*

    *  Restore probability models to the state before the recursive subdivision

    *  has been started.

    */

   wfa->states             = states;

   c->tree                 = *tree_model;

   c->p_tree               = *p_tree_model;

   c->domain_pool->model   = c->domain_pool->model_duplicate (domain_model);

   c->d_domain_pool->model = c->d_domain_pool->model_duplicate (d_domain_model);

   c->coeff->model   	   = c->coeff->model_duplicate (c->coeff, coeff_model);

   c->d_coeff->model   	   = c->d_coeff->model_duplicate (c->d_coeff,

							  d_coeff_model);

   if (c->mt->frame_type == I_FRAME)

      costs = nd_prediction (max_costs, price, band, y_state, range, wfa, c); 

   else

      costs = mc_prediction (max_costs, price, band, y_state, range, wfa, c);

   c->pixels = rec_pixels;

   if (costs < MAXCOSTS)

   {

      /*

       *  Free the memory used by the state_data struct

       */

      for (state = states; state < rec_states; state++)

      {

	 sd = &rec_state_data [state - states];

	 for (level = c->options.images_level + 1;

	      level <= c->options.lc_max_level; level++)

	    if (sd->ip_states_state [level] != NULL)

	       Free (sd->ip_states_state [level]);

	 if (sd->images_of_state != NULL)

	    Free (sd->images_of_state);

	 if (sd->inner_products != NULL)

	    Free (sd->inner_products);

      }

      if (states < rec_states)

	 Free (rec_state_data);

      c->domain_pool->model_free (rec_domain_model);

      c->d_domain_pool->model_free (rec_d_domain_model);

      c->coeff->model_free (rec_coeff_model);

      c->d_coeff->model_free (rec_d_coeff_model);

      costs = (range->tree_bits + range->matrix_bits + range->weights_bits

	       + range->mv_tree_bits + range->mv_coord_bits

	       + range->nd_tree_bits + range->nd_weights_bits) * price

	      + range->err;

   }

   else

   {

      /*

       *  Restore WFA to state before function was called

       */

      c->domain_pool->model_free (c->domain_pool->model);

      c->d_domain_pool->model_free (c->d_domain_pool->model);

      c->coeff->model_free (c->coeff->model);

      c->d_coeff->model_free (c->d_coeff->model);

      c->domain_pool->model   = rec_domain_model;

      c->d_domain_pool->model = rec_d_domain_model;

      c->coeff->model         = rec_coeff_model;

      c->d_coeff->model       = rec_d_coeff_model;

      c->tree                 = rec_tree_model;

      c->p_tree               = rec_p_tree_model;

      range->prediction = NO;

      if (wfa->states != states)

	 remove_states (states, wfa);

      restore_state_data (states, rec_states - 1, c->options.lc_max_level,

			  rec_state_data, wfa, c);

      costs = MAXCOSTS;

   }

   return costs;

} 

void

clear_norms_table (unsigned level, const wfa_info_t *wi, motion_t *mt)

/*

 *  Clear norms arrays.

 *

 *  No return value.

 */

{

   unsigned  range_size = wi->half_pixel

			  ? square (wi->search_range)

			  : square (2 * wi->search_range);

   if (level > wi->p_min_level)

   {

      memset (mt->mc_forward_norms [level], 0, range_size * sizeof(real_t));

      memset (mt->mc_backward_norms [level], 0, range_size * sizeof(real_t));

   }

}

void

update_norms_table (unsigned level, const wfa_info_t *wi, motion_t *mt)

/*

 *  Norms table of levels larger than the bottom level are computed

 *  by summing up previously calculated displacement costs of lower levels.

 *

 *  No return value.

 */

{

   unsigned  range_size = wi->half_pixel

			  ? square (wi->search_range)

			  : square (2 * wi->search_range);

   if (level > wi->p_min_level)

   {

      unsigned index;			/* index of motion vector */

      for (index = 0; index < range_size; index++)

	 mt->mc_forward_norms [level][index]

	    += mt->mc_forward_norms [level - 1][index];

      if (mt->frame_type == B_FRAME)

	 for (index = 0; index < range_size; index++)

	    mt->mc_backward_norms [level][index]

	       += mt->mc_backward_norms [level - 1][index];

   }

}

/*****************************************************************************

				private code

*****************************************************************************/

static real_t

mc_prediction (real_t max_costs, real_t price, unsigned band, int y_state,

	       range_t *range, wfa_t *wfa, coding_t *c)

{

   real_t    costs;		   	/* current approximation costs */

   range_t   prange = *range;

   unsigned  width  = width_of_level (range->level);

   unsigned  height = height_of_level (range->level);

   word_t   *mcpe   = Calloc (width * height, sizeof (word_t));

   /*

    *  If we are at the bottom level of the mc tree:

    *  Fill in the norms table

    */

   if (prange.level == wfa->wfainfo->p_min_level) 

      fill_norms_table (prange.x, prange.y, prange.level, wfa->wfainfo, c->mt);

   /*

    *  Predict 'range' with motion compensation according to frame type.

    *  MCPE is returned in 'c->mcpe'

    */

   if (c->mt->frame_type == P_FRAME)

      find_P_frame_mc (mcpe, price, &prange, wfa->wfainfo, c->mt);

   else

      find_B_frame_mc (mcpe, price, &prange, wfa->wfainfo, c->mt);

   costs = (prange.mv_tree_bits + prange.mv_coord_bits) * price;

   if (costs < max_costs)		/* motion vector not too expensive */

   {

      unsigned  last_state;		/* last WFA state before recursion */

      real_t   *ipi [MAXSTATES];	/* inner products pointers */

      unsigned  state;

      real_t  	mvt, mvc;

      c->pixels = Calloc (width * height, sizeof (real_t));

      cut_to_bintree (c->pixels, mcpe, width, height, 0, 0, width, height);

      /*

       *  Approximate MCPE recursively.

       */

      last_state = wfa->states - 1;

      for (state = 0; state <= last_state; state++)

	 if (need_image (state, wfa))

	 {

	    ipi [state] = c->ip_images_state[state];

	    c->ip_images_state[state]

	       = Calloc (size_of_tree (c->products_level), sizeof (real_t));

	 }

      mvc = prange.mv_coord_bits;

      mvt = prange.mv_tree_bits;

      prange.image           = 0;

      prange.address         = 0;

      prange.tree_bits       = 0;

      prange.matrix_bits     = 0;

      prange.weights_bits    = 0;

      prange.mv_coord_bits   = 0;

      prange.mv_tree_bits    = 0;

      prange.nd_weights_bits = 0;

      prange.nd_tree_bits    = 0;

      compute_ip_images_state (prange.image, prange.address, prange.level,

			       1, 0, wfa, c);

      costs += subdivide (max_costs - costs, band, y_state, &prange,

			  wfa, c, NO, YES);

      if (costs < max_costs)		/* use motion compensation */

      {

	 unsigned img, adr;		/* temp. values */

	 img                  = range->image;

	 adr                  = range->address;

	 *range               = prange;

	 range->image         = img;

	 range->address       = adr;

	 range->mv_coord_bits = mvc;

	 range->mv_tree_bits  = mvt;

	 range->prediction    = YES;

	 for (state = last_state + 1; state < wfa->states; state++)

	    if (need_image (state, wfa))

	       memset (c->ip_images_state [state], 0,

		       size_of_tree (c->products_level) * sizeof (real_t));

	 costs = (range->tree_bits + range->matrix_bits + range->weights_bits

		  + range->mv_tree_bits + range->mv_coord_bits

		  + range->nd_tree_bits + range->nd_weights_bits) * price

		 + range->err;

      }

      else

	 costs = MAXCOSTS;

      for (state = 0; state <= last_state; state++)

	 if (need_image (state, wfa))

	 {

	    Free (c->ip_images_state[state]);

	    c->ip_images_state[state] = ipi [state];

	 }

      Free (c->pixels);

   }

   else

      costs = MAXCOSTS;

   Free (mcpe);

   return costs;

}

static real_t

nd_prediction (real_t max_costs, real_t price, unsigned band, int y_state,

	       range_t *range, wfa_t *wfa, coding_t *c)

{

   real_t  costs;			/* current approximation costs */

   range_t lrange = *range;

   /*

    *  Predict 'range' with DC component approximation

    */

   {

      real_t x = get_ip_image_state (range->image, range->address,

				     range->level, 0, c);

      real_t y = get_ip_state_state (0, 0, range->level, c);

      real_t w = btor (rtob (x / y, c->coeff->dc_rpf), c->coeff->dc_rpf);

      word_t s [2] = {0, -1};

      lrange.into [0] 	     = 0;

      lrange.into [1] 	     = NO_EDGE;

      lrange.weight [0]      = w;

      lrange.mv_coord_bits   = 0;

      lrange.mv_tree_bits    = 0;

      lrange.nd_tree_bits    = tree_bits (LEAF, lrange.level, &c->p_tree);

      lrange.nd_weights_bits = 0;

      lrange.tree_bits       = 0;

      lrange.matrix_bits     = 0;

      lrange.weights_bits    = c->coeff->bits (&w, s, range->level, c->coeff);

   }

   costs = price * (lrange.weights_bits + lrange.nd_tree_bits);

   /*

    *  Recursive aproximation of difference image

    */

   if (costs < max_costs)		

   {

      unsigned  state;

      range_t  	rrange;			/* range: recursive subdivision */

      unsigned  last_state;		/* last WFA state before recursion */

      real_t   *ipi [MAXSTATES];	/* inner products pointers */

      unsigned 	width  = width_of_level (range->level);

      unsigned  height = height_of_level (range->level);

      real_t   *pixels;

      /*

       *  Generate difference image original - approximation

       */

      {

	 unsigned  n;

	 real_t *src, *dst;		/* pointers to image data */

	 real_t w = - lrange.weight [0] * c->images_of_state [0][0];

	 src = c->pixels + range->address * size_of_level (range->level); 

	 dst = c->pixels = pixels = Calloc (width * height, sizeof (real_t));

	 for (n = width * height; n; n--)

	    *dst++ = *src++ + w;

      }

      /*

       *  Approximate difference recursively.

       */

      rrange                 = *range;

      rrange.tree_bits       = 0;

      rrange.matrix_bits     = 0;

      rrange.weights_bits    = 0;

      rrange.mv_coord_bits   = 0;

      rrange.mv_tree_bits    = 0;

      rrange.nd_tree_bits    = 0;

      rrange.nd_weights_bits = 0;

      rrange.image           = 0;

      rrange.address         = 0;

      last_state = wfa->states - 1;

      for (state = 0; state <= last_state; state++)

	 if (need_image (state, wfa))

	 {

	    ipi [state] = c->ip_images_state[state];

	    c->ip_images_state[state]

	       = Calloc (size_of_tree (c->products_level), sizeof (real_t));

	 }

      compute_ip_images_state (rrange.image, rrange.address, rrange.level,

			       1, 0, wfa, c);

      costs += subdivide (max_costs - costs, band, y_state, &rrange, wfa, c,

			  NO, YES);

      Free (pixels);

      if (costs < max_costs && ischild (rrange.tree)) /* use prediction */

      {

	 unsigned img, adr;

	 unsigned edge;

	 img                     = range->image;

	 adr                     = range->address;

	 *range                  = rrange;

	 range->image            = img;

	 range->address          = adr;

	 range->nd_tree_bits    += lrange.nd_tree_bits;

	 range->nd_weights_bits += lrange.weights_bits;

	 for (edge = 0; isedge (lrange.into [edge]); edge++)

	 {

	    range->into [edge]   = lrange.into [edge];

	    range->weight [edge] = lrange.weight [edge];

	 }

	 range->into [edge] = NO_EDGE;

	 range->prediction  = edge;

	 for (state = last_state + 1; state < wfa->states; state++)

	    if (need_image (state, wfa))

	       memset (c->ip_images_state [state], 0,

		       size_of_tree (c->products_level) * sizeof (real_t));

      }

      else

	 costs = MAXCOSTS;

      for (state = 0; state <= last_state; state++)

	 if (need_image (state, wfa))

	 {

	    Free (c->ip_images_state [state]);

	    c->ip_images_state [state] = ipi [state];

	 }

   }

   else

      costs = MAXCOSTS;

   return costs;

}

static state_data_t *

store_state_data (unsigned from, unsigned to, unsigned max_level,

		  wfa_t *wfa, coding_t *c)

/*

 *  Save and remove all states starting from state 'from'.

 *

 *  Return value:

 *	pointer to array of state_data structs

 */

{

   state_data_t *data;			/* array of savestates */

   state_data_t *sd;			/* pointer to current savestates */

   unsigned	 state, label, level;

   if (to < from)

      return NULL;			/* nothing to do */

   data = Calloc (to - from + 1, sizeof (state_data_t));

   for (state = from; state <= to; state++)

   {

      sd = &data [state - from];

      sd->final_distribution = wfa->final_distribution [state];

      sd->level_of_state     = wfa->level_of_state [state];

      sd->domain_type        = wfa->domain_type [state];

      sd->images_of_state    = c->images_of_state [state];

      sd->inner_products     = c->ip_images_state [state];

      wfa->domain_type [state]   = 0;

      c->images_of_state [state] = NULL;

      c->ip_images_state [state] = NULL;

      for (label = 0; label < MAXLABELS; label++) 

      {

	 sd->tree [label]     	= wfa->tree [state][label];

	 sd->y_state [label]  	= wfa->y_state [state][label];

	 sd->y_column [label] 	= wfa->y_column [state][label];

	 sd->mv_tree [label]  	= wfa->mv_tree [state][label];

	 sd->x [label]        	= wfa->x [state][label];

	 sd->y [label]        	= wfa->y [state][label];

	 sd->prediction [label] = wfa->prediction [state][label];

	 memcpy (sd->weight [label], wfa->weight [state][label], 

		 sizeof (real_t) * (MAXEDGES + 1));

	 memcpy (sd->int_weight [label], wfa->int_weight [state][label], 

		 sizeof (word_t) * (MAXEDGES + 1));

	 memcpy (sd->into [label], wfa->into [state][label], 

		 sizeof (word_t) * (MAXEDGES + 1));

	 wfa->into [state][label][0] = NO_EDGE;

	 wfa->tree [state][label]    = RANGE;

	 wfa->y_state [state][label] = RANGE;

      }

      for (level = c->options.images_level + 1; level <= max_level;

	   level++)

      {

	 sd->ip_states_state [level]       = c->ip_states_state [state][level];

	 c->ip_states_state [state][level] = NULL;

      }

   }

   return data;

}

static void

restore_state_data (unsigned from, unsigned to, unsigned max_level,

		    state_data_t *data, wfa_t *wfa, coding_t *c)

/*

 *  Restore all state data starting from state 'from'.

 *  

 *  No return value.

 */

{

   state_data_t *sd;			/* pointer to state_data item */

   unsigned	 state, label, level;

   if (to < from)

      return;				/* nothing to do */

   for (state = from; state <= to; state++)

   {

      sd = &data [state - from];

      wfa->final_distribution [state] = sd->final_distribution;

      wfa->level_of_state [state]     = sd->level_of_state;

      wfa->domain_type [state]        = sd->domain_type;

      if (c->images_of_state [state] != NULL)

	 Free (c->images_of_state [state]);

      c->images_of_state [state] = sd->images_of_state;

      if (c->ip_images_state [state] != NULL)

	 Free (c->ip_images_state [state]);

      c->ip_images_state [state] = sd->inner_products;

      for (label = 0; label < MAXLABELS; label++)

      {

	 wfa->tree [state][label]     	= sd->tree [label];

	 wfa->y_state [state][label]  	= sd->y_state [label];

	 wfa->y_column [state][label] 	= sd->y_column [label];

	 wfa->mv_tree [state][label]  	= sd->mv_tree [label];

	 wfa->x [state][label]        	= sd->x [label];

	 wfa->y [state][label]        	= sd->y [label];

	 wfa->prediction [state][label] = sd->prediction [label];

	 memcpy (wfa->weight [state][label], sd->weight [label], 

		 sizeof(real_t) * (MAXEDGES + 1));

	 memcpy (wfa->int_weight [state][label], sd->int_weight [label], 

		 sizeof(word_t) * (MAXEDGES + 1));

	 memcpy (wfa->into [state][label], sd->into [label],  

		 sizeof(word_t) * (MAXEDGES + 1));

      }	 

      for (level = c->options.images_level + 1; level <= max_level;

	   level++)

      {

	 if (c->ip_states_state [state][level] != NULL)

	    Free (c->ip_states_state [state][level]);

	 c->ip_states_state [state][level] = sd->ip_states_state [level];

      }

   }

   Free (data);

   wfa->states = to + 1;

}