public override void Compress3(ref byte[] block, int offset)
{
// build the table of lookups
SingleColourLookup[][] lookups = new SingleColourLookup[][]
{
SingleColourLookupIns.lookup_5_3,
SingleColourLookupIns.lookup_6_3,
SingleColourLookupIns.lookup_5_3
};
// find the best end-points and index
ComputeEndPoints(lookups);
// build the block if we win
if (m_error < m_besterror)
{
// remap the indices
byte[] indices = new byte[16];
m_colours.RemapIndices(new byte[] { m_index }, indices);
// save the block
ColourBlock.WriteColourBlock3(m_start, m_end, indices, ref block, offset);
// save the error
m_besterror = m_error;
}
}
public override void Compress3(ref byte[] block, int offset)
{
// cache some values
int count = m_colours.Count;
Vector3[] values = m_colours.Points;
// create a codebook
Vector3[] codes = new Vector3[3];
codes[0] = m_start;
codes[1] = m_end;
codes[2] = 0.5f * m_start + 0.5f * m_end;
// match each point to the closest code
byte[] closest = new byte[16];
float error = 0.0f;
for (int i = 0; i < count; ++i)
{
// find the closest code
float dist = float.MaxValue;
int idx = 0;
for (int j = 0; j < 3; ++j)
{
float d = (m_metric * (values[i] - codes[j])).LengthSquared();
if (d < dist)
{
dist = d;
idx = j;
}
}
// save the index
closest[i] = (byte)idx;
// accumulate the error
error += dist;
}
// save this scheme if it wins
if (error < m_besterror)
{
// remap the indices
byte[] indices = new byte[16];
m_colours.RemapIndices(closest, indices);
// save the block
ColourBlock.WriteColourBlock3(m_start, m_end, indices, ref block, offset);
// save the error
m_besterror = error;
}
}
public override void Compress3(ref byte[] block, int offset)
{
// declare variables
int count = m_colours.Count;
Vector4 two = new Vector4(2.0f);
Vector4 one = new Vector4(1.0f);
Vector4 half_half2 = new Vector4(0.5f, 0.5f, 0.5f, 0.25f);
Vector4 zero = new Vector4(0.0f);
Vector4 half = new Vector4(0.5f);
Vector4 grid = new Vector4(31.0f, 63.0f, 31.0f, 0.0f);
Vector4 gridrcp = new Vector4(1.0f / 31.0f, 1.0f / 63.0f, 1.0f / 31.0f, 0.0f);
// prepare an ordering using the principle axis
ConstructOrdering(m_principle, 0);
// check all possible clusters and iterate on the total order
Vector4 beststart = new Vector4(0.0f);
Vector4 bestend = new Vector4(0.0f);
Vector4 besterror = m_besterror;
byte[] bestindices = new byte[16];
int bestiteration = 0;
int besti = 0, bestj = 0;
// loop over iterations (we avoid the case that all points in first or last cluster)
for (int iterationIndex = 0; ;)
{
// first cluster [0,i) is at the start
Vector4 part0 = new Vector4(0.0f);
for (int i = 0; i < count; ++i)
{
// second cluster [i,j) is half along
Vector4 part1 = (i == 0) ? m_points_weights[0] : new Vector4(0.0f);
int jmin = (i == 0) ? 1 : i;
for (int j = jmin; ;)
{
// last cluster [j,count) is at the end
Vector4 part2 = m_xsum_wsum - part1 - part0;
// compute least squares terms directly
Vector4 alphax_sum = Helpers.MultiplyAdd(part1, half_half2, part0);
Vector4 alpha2_sum = alphax_sum.SplatW();
Vector4 betax_sum = Helpers.MultiplyAdd(part1, half_half2, part2);
Vector4 beta2_sum = betax_sum.SplatW();
Vector4 alphabeta_sum = (part1 * half_half2).SplatW();
// compute the least-squares optimal points
Vector4 factor = Helpers.Reciprocal(Helpers.NegativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum * beta2_sum));
Vector4 a = Helpers.NegativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum * beta2_sum) * factor;
Vector4 b = Helpers.NegativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum * alpha2_sum) * factor;
// clamp to the grid
a = Vector4.Min(one, Vector4.Max(zero, a));
b = Vector4.Min(one, Vector4.Max(zero, b));
a = Helpers.Truncate(Helpers.MultiplyAdd(grid, a, half)) * gridrcp;
b = Helpers.Truncate(Helpers.MultiplyAdd(grid, b, half)) * gridrcp;
// compute the error (we skip the constant xxsum)
Vector4 e1 = Helpers.MultiplyAdd(a * a, alpha2_sum, b * b * beta2_sum);
Vector4 e2 = Helpers.NegativeMultiplySubtract(a, alphax_sum, a * b * alphabeta_sum);
Vector4 e3 = Helpers.NegativeMultiplySubtract(b, betax_sum, e2);
Vector4 e4 = Helpers.MultiplyAdd(two, e3, e1);
// apply the metric to the error term
Vector4 e5 = e4 * m_metric;
Vector4 error = e5.SplatX() + e5.SplatY() + e5.SplatZ();
// keep the solution if it wins
if (Helpers.CompareAnyLessThan(error, besterror))
{
beststart = a;
bestend = b;
besti = i;
bestj = j;
besterror = error;
bestiteration = iterationIndex;
}
// advance
if (j == count)
{
break;
}
part1 += m_points_weights[j];
++j;
}
// advance
part0 += m_points_weights[i];
}
// stop if we didn't improve in this iteration
if (bestiteration != iterationIndex)
{
break;
}
// advance if possible
++iterationIndex;
if (iterationIndex == m_iterationCount)
{
break;
}
// stop if a new iteration is an ordering that has already been tried
Vector3 axis = (bestend - beststart).ToVector3();
if (!ConstructOrdering(axis, iterationIndex))
{
break;
}
}
// save the block if necessary
if (Helpers.CompareAnyLessThan(besterror, m_besterror))
{
byte[] unordered = new byte[16];
for (int m = 0; m < besti; ++m)
{
unordered[m_order[(16 * bestiteration) + m]] = 0;
}
for (int m = besti; m < bestj; ++m)
{
unordered[m_order[(16 * bestiteration) + m]] = 2;
}
for (int m = bestj; m < count; ++m)
{
unordered[m_order[(16 * bestiteration) + m]] = 1;
}
m_colours.RemapIndices(unordered, bestindices);
// save the block
ColourBlock.WriteColourBlock3(beststart.ToVector3(), bestend.ToVector3(), bestindices, ref block, offset);
// save the error
m_besterror = besterror;
}
}
