draco/compression/attributes/prediction_schemes/mesh_prediction_scheme_parallelogram.h

// Copyright 2016 The Draco Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#ifndef DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_PARALLELOGRAM_H_
#define DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_PARALLELOGRAM_H_

#include "compression/attributes/prediction_schemes/mesh_prediction_scheme.h"
#include "compression/attributes/prediction_schemes/mesh_prediction_scheme_parallelogram_shared.h"

namespace draco {

// Parallelogram prediction predicts an attribute value V from three vertices
// on the opposite face to the predicted vertex. The values on the three
// vertices are used to construct a parallelogram V' = O - A - B, where O is the
// value on the oppoiste vertex, and A, B are values on the shared vertices:
//     V
//    / \
//   /   \
//  /     \
// A-------B
//  \     /
//   \   /
//    \ /
//     O

template <typename DataTypeT, class TransformT, class MeshDataT>
class MeshPredictionSchemeParallelogram
    : public MeshPredictionScheme<DataTypeT, TransformT, MeshDataT> {
 public:
  using CorrType = typename PredictionScheme<DataTypeT, TransformT>::CorrType;
  using CornerTable = typename MeshDataT::CornerTable;
  explicit MeshPredictionSchemeParallelogram(const PointAttribute *attribute)
      : MeshPredictionScheme<DataTypeT, TransformT, MeshDataT>(attribute) {}
  MeshPredictionSchemeParallelogram(const PointAttribute *attribute,
                                    const TransformT &transform,
                                    const MeshDataT &mesh_data)
      : MeshPredictionScheme<DataTypeT, TransformT, MeshDataT>(
            attribute, transform, mesh_data) {}

  bool Encode(const DataTypeT *in_data, CorrType *out_corr, int size,
              int num_components,
              const PointIndex *entry_to_point_id_map) override;
  bool Decode(const CorrType *in_corr, DataTypeT *out_data, int size,
              int num_components,
              const PointIndex *entry_to_point_id_map) override;
  PredictionSchemeMethod GetPredictionMethod() const override {
    return MESH_PREDICTION_PARALLELOGRAM;
  }

  bool IsInitialized() const override {
    return this->mesh_data().IsInitialized();
  }
};

template <typename DataTypeT, class TransformT, class MeshDataT>
bool MeshPredictionSchemeParallelogram<DataTypeT, TransformT, MeshDataT>::
    Encode(const DataTypeT *in_data, CorrType *out_corr, int size,
           int num_components, const PointIndex *entry_to_point_id_map) {
  this->transform().InitializeEncoding(in_data, size, num_components);
  std::unique_ptr<DataTypeT[]> pred_vals(new DataTypeT[num_components]());

  // We start processing from the end because this prediction uses data from
  // previous entries that could be overwritten when an entry is processed.
  const CornerTable *const table = this->mesh_data().corner_table();
  const std::vector<int32_t> *const vertex_to_data_map =
      this->mesh_data().vertex_to_data_map();
  for (int p = this->mesh_data().data_to_corner_map()->size() - 1; p > 0; --p) {
    const CornerIndex corner_id = this->mesh_data().data_to_corner_map()->at(p);
    // Initialize the vertex ids to "p" which ensures that if the opposite
    // corner does not exist we will not use the vertices to predict the
    // encoded value.
    int vert_opp = p, vert_next = p, vert_prev = p;
    const CornerIndex opp_corner = table->Opposite(corner_id);
    if (opp_corner >= 0) {
      // Get vertices on the opposite face.
      GetParallelogramEntries(opp_corner, table, *vertex_to_data_map, &vert_opp,
                              &vert_next, &vert_prev);
    }
    const int dst_offset = p * num_components;
    if (vert_opp >= p || vert_next >= p || vert_prev >= p) {
      // Some of the vertices are not valid (not encoded yet).
      // We use the last encoded point as a reference.
      const int src_offset = (p - 1) * num_components;
      this->transform().ComputeCorrection(
          in_data + dst_offset, in_data + src_offset, out_corr, dst_offset);
    } else {
      // Apply the parallelogram prediction.
      const int v_opp_off = vert_opp * num_components;
      const int v_next_off = vert_next * num_components;
      const int v_prev_off = vert_prev * num_components;
      for (int c = 0; c < num_components; ++c) {
        pred_vals[c] = (in_data[v_next_off + c] + in_data[v_prev_off + c]) -
                       in_data[v_opp_off + c];
      }
      this->transform().ComputeCorrection(in_data + dst_offset, pred_vals.get(),
                                          out_corr, dst_offset);
    }
  }
  // First element is always fixed because it cannot be predicted.
  for (int i = 0; i < num_components; ++i) {
    pred_vals[i] = static_cast<DataTypeT>(0);
  }
  this->transform().ComputeCorrection(in_data, pred_vals.get(), out_corr, 0);
  return true;
}

template <typename DataTypeT, class TransformT, class MeshDataT>
bool MeshPredictionSchemeParallelogram<DataTypeT, TransformT, MeshDataT>::
    Decode(const CorrType *in_corr, DataTypeT *out_data, int size,
           int num_components, const PointIndex *entry_to_point_id_map) {
  this->transform().InitializeDecoding(num_components);

  const CornerTable *const table = this->mesh_data().corner_table();
  const std::vector<int32_t> *const vertex_to_data_map =
      this->mesh_data().vertex_to_data_map();

  std::unique_ptr<DataTypeT[]> pred_vals(new DataTypeT[num_components]());

  // Restore the first value.
  this->transform().ComputeOriginalValue(pred_vals.get(), in_corr, out_data, 0);

  for (int p = 1; p < this->mesh_data().data_to_corner_map()->size(); ++p) {
    const CornerIndex corner_id = this->mesh_data().data_to_corner_map()->at(p);
    int vert_opp = p, vert_next = p, vert_prev = p;
    const CornerIndex opp_corner = table->Opposite(corner_id);
    if (opp_corner >= 0) {
      // Get vertices on the opposite face.
      GetParallelogramEntries(opp_corner, table, *vertex_to_data_map, &vert_opp,
                              &vert_next, &vert_prev);
    }
    const int dst_offset = p * num_components;
    if (vert_opp >= p || vert_next >= p || vert_prev >= p) {
      // Some of the vertices are not valid (not decoded yet).
      // We use the last decoded point as a reference.
      const int src_offset = (p - 1) * num_components;
      this->transform().ComputeOriginalValue(out_data + src_offset, in_corr,
                                             out_data + dst_offset, dst_offset);
    } else {
      // Apply the parallelogram prediction.
      const int v_opp_off = vert_opp * num_components;
      const int v_next_off = vert_next * num_components;
      const int v_prev_off = vert_prev * num_components;
      for (int c = 0; c < num_components; ++c) {
        pred_vals[c] = (out_data[v_next_off + c] + out_data[v_prev_off + c]) -
                       out_data[v_opp_off + c];
      }
      this->transform().ComputeOriginalValue(pred_vals.get(), in_corr,
                                             out_data + dst_offset, dst_offset);
    }
  }
  return true;
}

}  // namespace draco

#endif  // DRACO_COMPRESSION_ATTRIBUTES_PREDICTION_SCHEMES_MESH_PREDICTION_SCHEME_PARALLELOGRAM_H_