393 lines
14 KiB
C++
393 lines
14 KiB
C++
/* -----------------------------------------------------------------------------
|
|
|
|
Copyright (c) 2006 Simon Brown si@sjbrown.co.uk
|
|
Copyright (c) 2007 Ignacio Castano icastano@nvidia.com
|
|
|
|
Permission is hereby granted, free of charge, to any person obtaining
|
|
a copy of this software and associated documentation files (the
|
|
"Software"), to deal in the Software without restriction, including
|
|
without limitation the rights to use, copy, modify, merge, publish,
|
|
distribute, sublicense, and/or sell copies of the Software, and to
|
|
permit persons to whom the Software is furnished to do so, subject to
|
|
the following conditions:
|
|
|
|
The above copyright notice and this permission notice shall be included
|
|
in all copies or substantial portions of the Software.
|
|
|
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
|
|
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
|
|
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
|
|
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
|
|
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
|
|
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
|
|
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
|
|
|
-------------------------------------------------------------------------- */
|
|
|
|
#include "clusterfit.h"
|
|
#include "colourset.h"
|
|
#include "colourblock.h"
|
|
#include <cfloat>
|
|
|
|
namespace squish {
|
|
|
|
ClusterFit::ClusterFit( ColourSet const* colours, int flags, float* metric )
|
|
: ColourFit( colours, flags )
|
|
{
|
|
// set the iteration count
|
|
m_iterationCount = ( m_flags & kColourIterativeClusterFit ) ? kMaxIterations : 1;
|
|
|
|
// initialise the metric (old perceptual = 0.2126f, 0.7152f, 0.0722f)
|
|
if( metric )
|
|
m_metric = Vec4( metric[0], metric[1], metric[2], 1.0f );
|
|
else
|
|
m_metric = VEC4_CONST( 1.0f );
|
|
|
|
// initialise the best error
|
|
m_besterror = VEC4_CONST( FLT_MAX );
|
|
|
|
// cache some values
|
|
int const count = m_colours->GetCount();
|
|
Vec3 const* values = m_colours->GetPoints();
|
|
|
|
// get the covariance matrix
|
|
Sym3x3 covariance = ComputeWeightedCovariance( count, values, m_colours->GetWeights() );
|
|
|
|
// compute the principle component
|
|
m_principle = ComputePrincipleComponent( covariance );
|
|
}
|
|
|
|
bool ClusterFit::ConstructOrdering( Vec3 const& axis, int iteration )
|
|
{
|
|
// cache some values
|
|
int const count = m_colours->GetCount();
|
|
Vec3 const* values = m_colours->GetPoints();
|
|
|
|
// build the list of dot products
|
|
float dps[16];
|
|
u8* order = ( u8* )m_order + 16*iteration;
|
|
for( int i = 0; i < count; ++i )
|
|
{
|
|
dps[i] = Dot( values[i], axis );
|
|
order[i] = ( u8 )i;
|
|
}
|
|
|
|
// stable sort using them
|
|
for( int i = 0; i < count; ++i )
|
|
{
|
|
for( int j = i; j > 0 && dps[j] < dps[j - 1]; --j )
|
|
{
|
|
std::swap( dps[j], dps[j - 1] );
|
|
std::swap( order[j], order[j - 1] );
|
|
}
|
|
}
|
|
|
|
// check this ordering is unique
|
|
for( int it = 0; it < iteration; ++it )
|
|
{
|
|
u8 const* prev = ( u8* )m_order + 16*it;
|
|
bool same = true;
|
|
for( int i = 0; i < count; ++i )
|
|
{
|
|
if( order[i] != prev[i] )
|
|
{
|
|
same = false;
|
|
break;
|
|
}
|
|
}
|
|
if( same )
|
|
return false;
|
|
}
|
|
|
|
// copy the ordering and weight all the points
|
|
Vec3 const* unweighted = m_colours->GetPoints();
|
|
float const* weights = m_colours->GetWeights();
|
|
m_xsum_wsum = VEC4_CONST( 0.0f );
|
|
for( int i = 0; i < count; ++i )
|
|
{
|
|
int j = order[i];
|
|
Vec4 p( unweighted[j].X(), unweighted[j].Y(), unweighted[j].Z(), 1.0f );
|
|
Vec4 w( weights[j] );
|
|
Vec4 x = p*w;
|
|
m_points_weights[i] = x;
|
|
m_xsum_wsum += x;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void ClusterFit::Compress3( void* block )
|
|
{
|
|
// declare variables
|
|
int const count = m_colours->GetCount();
|
|
Vec4 const two = VEC4_CONST( 2.0 );
|
|
Vec4 const one = VEC4_CONST( 1.0f );
|
|
Vec4 const half_half2( 0.5f, 0.5f, 0.5f, 0.25f );
|
|
Vec4 const zero = VEC4_CONST( 0.0f );
|
|
Vec4 const half = VEC4_CONST( 0.5f );
|
|
Vec4 const grid( 31.0f, 63.0f, 31.0f, 0.0f );
|
|
Vec4 const gridrcp( 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
|
|
Vec4 beststart = VEC4_CONST( 0.0f );
|
|
Vec4 bestend = VEC4_CONST( 0.0f );
|
|
Vec4 besterror = m_besterror;
|
|
u8 bestindices[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
|
|
Vec4 part0 = VEC4_CONST( 0.0f );
|
|
for( int i = 0; i < count; ++i )
|
|
{
|
|
// second cluster [i,j) is half along
|
|
Vec4 part1 = ( i == 0 ) ? m_points_weights[0] : VEC4_CONST( 0.0f );
|
|
int jmin = ( i == 0 ) ? 1 : i;
|
|
for( int j = jmin;; )
|
|
{
|
|
// last cluster [j,count) is at the end
|
|
Vec4 part2 = m_xsum_wsum - part1 - part0;
|
|
|
|
// compute least squares terms directly
|
|
Vec4 alphax_sum = MultiplyAdd( part1, half_half2, part0 );
|
|
Vec4 alpha2_sum = alphax_sum.SplatW();
|
|
|
|
Vec4 betax_sum = MultiplyAdd( part1, half_half2, part2 );
|
|
Vec4 beta2_sum = betax_sum.SplatW();
|
|
|
|
Vec4 alphabeta_sum = ( part1*half_half2 ).SplatW();
|
|
|
|
// compute the least-squares optimal points
|
|
Vec4 factor = Reciprocal( NegativeMultiplySubtract( alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum ) );
|
|
Vec4 a = NegativeMultiplySubtract( betax_sum, alphabeta_sum, alphax_sum*beta2_sum )*factor;
|
|
Vec4 b = NegativeMultiplySubtract( alphax_sum, alphabeta_sum, betax_sum*alpha2_sum )*factor;
|
|
|
|
// clamp to the grid
|
|
a = Min( one, Max( zero, a ) );
|
|
b = Min( one, Max( zero, b ) );
|
|
a = Truncate( MultiplyAdd( grid, a, half ) )*gridrcp;
|
|
b = Truncate( MultiplyAdd( grid, b, half ) )*gridrcp;
|
|
|
|
// compute the error (we skip the constant xxsum)
|
|
Vec4 e1 = MultiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
|
|
Vec4 e2 = NegativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
|
|
Vec4 e3 = NegativeMultiplySubtract( b, betax_sum, e2 );
|
|
Vec4 e4 = MultiplyAdd( two, e3, e1 );
|
|
|
|
// apply the metric to the error term
|
|
Vec4 e5 = e4*m_metric;
|
|
Vec4 error = e5.SplatX() + e5.SplatY() + e5.SplatZ();
|
|
|
|
// keep the solution if it wins
|
|
if( 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
|
|
Vec3 axis = ( bestend - beststart ).GetVec3();
|
|
if( !ConstructOrdering( axis, iterationIndex ) )
|
|
break;
|
|
}
|
|
|
|
// save the block if necessary
|
|
if( CompareAnyLessThan( besterror, m_besterror ) )
|
|
{
|
|
// remap the indices
|
|
u8 const* order = ( u8* )m_order + 16*bestiteration;
|
|
|
|
u8 unordered[16];
|
|
for( int m = 0; m < besti; ++m )
|
|
unordered[order[m]] = 0;
|
|
for( int m = besti; m < bestj; ++m )
|
|
unordered[order[m]] = 2;
|
|
for( int m = bestj; m < count; ++m )
|
|
unordered[order[m]] = 1;
|
|
|
|
m_colours->RemapIndices( unordered, bestindices );
|
|
|
|
// save the block
|
|
WriteColourBlock3( beststart.GetVec3(), bestend.GetVec3(), bestindices, block );
|
|
|
|
// save the error
|
|
m_besterror = besterror;
|
|
}
|
|
}
|
|
|
|
void ClusterFit::Compress4( void* block )
|
|
{
|
|
// declare variables
|
|
int const count = m_colours->GetCount();
|
|
Vec4 const two = VEC4_CONST( 2.0f );
|
|
Vec4 const one = VEC4_CONST( 1.0f );
|
|
Vec4 const onethird_onethird2( 1.0f/3.0f, 1.0f/3.0f, 1.0f/3.0f, 1.0f/9.0f );
|
|
Vec4 const twothirds_twothirds2( 2.0f/3.0f, 2.0f/3.0f, 2.0f/3.0f, 4.0f/9.0f );
|
|
Vec4 const twonineths = VEC4_CONST( 2.0f/9.0f );
|
|
Vec4 const zero = VEC4_CONST( 0.0f );
|
|
Vec4 const half = VEC4_CONST( 0.5f );
|
|
Vec4 const grid( 31.0f, 63.0f, 31.0f, 0.0f );
|
|
Vec4 const gridrcp( 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
|
|
Vec4 beststart = VEC4_CONST( 0.0f );
|
|
Vec4 bestend = VEC4_CONST( 0.0f );
|
|
Vec4 besterror = m_besterror;
|
|
u8 bestindices[16];
|
|
int bestiteration = 0;
|
|
int besti = 0, bestj = 0, bestk = 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
|
|
Vec4 part0 = VEC4_CONST( 0.0f );
|
|
for( int i = 0; i < count; ++i )
|
|
{
|
|
// second cluster [i,j) is one third along
|
|
Vec4 part1 = VEC4_CONST( 0.0f );
|
|
for( int j = i;; )
|
|
{
|
|
// third cluster [j,k) is two thirds along
|
|
Vec4 part2 = ( j == 0 ) ? m_points_weights[0] : VEC4_CONST( 0.0f );
|
|
int kmin = ( j == 0 ) ? 1 : j;
|
|
for( int k = kmin;; )
|
|
{
|
|
// last cluster [k,count) is at the end
|
|
Vec4 part3 = m_xsum_wsum - part2 - part1 - part0;
|
|
|
|
// compute least squares terms directly
|
|
Vec4 const alphax_sum = MultiplyAdd( part2, onethird_onethird2, MultiplyAdd( part1, twothirds_twothirds2, part0 ) );
|
|
Vec4 const alpha2_sum = alphax_sum.SplatW();
|
|
|
|
Vec4 const betax_sum = MultiplyAdd( part1, onethird_onethird2, MultiplyAdd( part2, twothirds_twothirds2, part3 ) );
|
|
Vec4 const beta2_sum = betax_sum.SplatW();
|
|
|
|
Vec4 const alphabeta_sum = twonineths*( part1 + part2 ).SplatW();
|
|
|
|
// compute the least-squares optimal points
|
|
Vec4 factor = Reciprocal( NegativeMultiplySubtract( alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum ) );
|
|
Vec4 a = NegativeMultiplySubtract( betax_sum, alphabeta_sum, alphax_sum*beta2_sum )*factor;
|
|
Vec4 b = NegativeMultiplySubtract( alphax_sum, alphabeta_sum, betax_sum*alpha2_sum )*factor;
|
|
|
|
// clamp to the grid
|
|
a = Min( one, Max( zero, a ) );
|
|
b = Min( one, Max( zero, b ) );
|
|
a = Truncate( MultiplyAdd( grid, a, half ) )*gridrcp;
|
|
b = Truncate( MultiplyAdd( grid, b, half ) )*gridrcp;
|
|
|
|
// compute the error (we skip the constant xxsum)
|
|
Vec4 e1 = MultiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
|
|
Vec4 e2 = NegativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
|
|
Vec4 e3 = NegativeMultiplySubtract( b, betax_sum, e2 );
|
|
Vec4 e4 = MultiplyAdd( two, e3, e1 );
|
|
|
|
// apply the metric to the error term
|
|
Vec4 e5 = e4*m_metric;
|
|
Vec4 error = e5.SplatX() + e5.SplatY() + e5.SplatZ();
|
|
|
|
// keep the solution if it wins
|
|
if( CompareAnyLessThan( error, besterror ) )
|
|
{
|
|
beststart = a;
|
|
bestend = b;
|
|
besterror = error;
|
|
besti = i;
|
|
bestj = j;
|
|
bestk = k;
|
|
bestiteration = iterationIndex;
|
|
}
|
|
|
|
// advance
|
|
if( k == count )
|
|
break;
|
|
part2 += m_points_weights[k];
|
|
++k;
|
|
}
|
|
|
|
// 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
|
|
Vec3 axis = ( bestend - beststart ).GetVec3();
|
|
if( !ConstructOrdering( axis, iterationIndex ) )
|
|
break;
|
|
}
|
|
|
|
// save the block if necessary
|
|
if( CompareAnyLessThan( besterror, m_besterror ) )
|
|
{
|
|
// remap the indices
|
|
u8 const* order = ( u8* )m_order + 16*bestiteration;
|
|
|
|
u8 unordered[16];
|
|
for( int m = 0; m < besti; ++m )
|
|
unordered[order[m]] = 0;
|
|
for( int m = besti; m < bestj; ++m )
|
|
unordered[order[m]] = 2;
|
|
for( int m = bestj; m < bestk; ++m )
|
|
unordered[order[m]] = 3;
|
|
for( int m = bestk; m < count; ++m )
|
|
unordered[order[m]] = 1;
|
|
|
|
m_colours->RemapIndices( unordered, bestindices );
|
|
|
|
// save the block
|
|
WriteColourBlock4( beststart.GetVec3(), bestend.GetVec3(), bestindices, block );
|
|
|
|
// save the error
|
|
m_besterror = besterror;
|
|
}
|
|
}
|
|
|
|
} // namespace squish
|