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model.hh
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model.hh
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#ifndef MODEL_HH
#define MODEL_HH
#include <vector>
#include <memory>
#include "../util/debug.hh"
#include "../util/options.hh"
#include "../util/nd_array.hh"
#include "../../lepton/idct.hh"
#include "numeric.hh"
#include "branch.hh"
#include "../util/aligned_block.hh"
#include "../util/block_based_image.hh"
#ifndef USE_SCALAR
#include <tmmintrin.h>
#include "../util/mm_mullo_epi32.hh"
#endif
class BoolEncoder;
constexpr bool advanced_dc_prediction = true;
enum TableParams : unsigned int {
MAX_EXPONENT = 11,
BLOCK_TYPES = 2, // setting this to 3 gives us ~1% savings.. 2/3 from BLOCK_TYPES=2
NUM_NONZEROS_BINS = 10,
BSR_BEST_PRIOR_MAX = 11, // 1023 requires 11 bits to describe
band_divisor = 1,
COEF_BANDS = 64 / band_divisor,
ENTROPY_NODES = 15,
NUM_NONZEROS_EOB_PRIORS = 66,
ZERO_OR_EOB = 3,
RESIDUAL_NOISE_FLOOR = 7,
COEF_BITS = MAX_EXPONENT - 1, // the last item of the length is always 1
};
int get_sum_median_8(int16_t*data16i);
void set_branch_range_identity(Branch *start, Branch* end);
template <class BranchArray> void set_branch_array_identity(BranchArray &branches) {
auto begin = branches.begin();
auto end = branches.end();
set_branch_range_identity(begin, end);
/*
for (;false&&begin != end; ++begin) {
begin->set_identity();
}*/
}
struct Model
{
typedef Sirikata::Array4d<Branch, BLOCK_TYPES, 26, 6, 32> NonzeroCounts7x7;
NonzeroCounts7x7 num_nonzeros_counts_7x7_;
typedef Sirikata::Array5d<Branch, BLOCK_TYPES, 8, 8, 3, 4> NonzeroCounts1x8;
NonzeroCounts1x8 num_nonzeros_counts_1x8_;
NonzeroCounts1x8 num_nonzeros_counts_8x1_;
typedef Sirikata::Array4d<Branch,
BLOCK_TYPES,
COEF_BANDS,
(8 > NUM_NONZEROS_BINS?8:(unsigned int)NUM_NONZEROS_BINS),
COEF_BITS> ResidualNoiseCounts;
ResidualNoiseCounts residual_noise_counts_;
typedef Sirikata::Array2d<Branch,
NUMERIC_LENGTH_MAX,
COEF_BITS> ResidualNoiseCountsDc;
ResidualNoiseCountsDc residual_noise_counts_dc_;
typedef Sirikata::Array4d<Branch,
BLOCK_TYPES,
(1<<(1 + RESIDUAL_NOISE_FLOOR)),
1 + RESIDUAL_NOISE_FLOOR,
1<<RESIDUAL_NOISE_FLOOR > ResidualThresholdCounts;
ResidualThresholdCounts residual_threshold_counts_;
typedef Sirikata::Array5d<Branch,
BLOCK_TYPES,
NUM_NONZEROS_BINS,
15,
NUMERIC_LENGTH_MAX,
MAX_EXPONENT> ExponentCounts8;
typedef Sirikata::Array5d<Branch,
BLOCK_TYPES,
NUM_NONZEROS_BINS,
49,
NUMERIC_LENGTH_MAX,
MAX_EXPONENT> ExponentCounts7x7;
typedef Sirikata::Array3d<Branch,
((unsigned int)NUM_NONZEROS_BINS <= (unsigned int)NUMERIC_LENGTH_MAX
? (unsigned int)NUMERIC_LENGTH_MAX : (unsigned int)NUM_NONZEROS_BINS),
17/*any 16 bit number should fit*/,
MAX_EXPONENT> ExponentCountsDC;
ExponentCounts7x7 exponent_counts_;
ExponentCounts8 exponent_counts_x_;
ExponentCountsDC exponent_counts_dc_;
void set_tables_identity() {
set_branch_array_identity(num_nonzeros_counts_7x7_);
set_branch_array_identity(num_nonzeros_counts_1x8_);
set_branch_array_identity(num_nonzeros_counts_8x1_);
set_branch_array_identity(residual_noise_counts_);
set_branch_array_identity(residual_noise_counts_dc_);
set_branch_array_identity(residual_threshold_counts_);
set_branch_array_identity(exponent_counts_);
set_branch_array_identity(exponent_counts_x_);
set_branch_array_identity(exponent_counts_dc_);
set_branch_array_identity(sign_counts_);
}
typedef Sirikata::Array3d<Branch, BLOCK_TYPES, 4, NUMERIC_LENGTH_MAX> SignCounts;
SignCounts sign_counts_;
template <typename lambda>
void forall( const lambda & proc )
{
num_nonzeros_counts_7x7_.foreach(proc);
num_nonzeros_counts_1x8_.foreach(proc);
num_nonzeros_counts_8x1_.foreach(proc);
exponent_counts_x_.foreach(proc);
exponent_counts_.foreach(proc);
exponent_counts_dc_.foreach(proc);
residual_noise_counts_.foreach(proc);
residual_threshold_counts_.foreach(proc);
sign_counts_.foreach(proc);
}
enum Printability{
PRINTABLE_INSIGNIFICANT = 1,
PRINTABLE_OK = 2,
CLOSE_TO_50 = 4,
CLOSE_TO_ONE_ANOTHER = 8
};
struct PrintabilitySpecification {
uint64_t printability_bitmask;
double tolerance;
uint64_t min_samples;
};
const Model& debug_print(const Model* other, PrintabilitySpecification spec)const;
};
enum ContextTypes{
ZDSTSCAN,
ZEROS7x7,
EXPDC,
RESDC,
SIGNDC,
EXP7x7,
RES7x7,
SIGN7x7,
ZEROS1x8,
ZEROS8x1,
EXP8,
THRESH8,
RES8,
SIGN8,
NUMCONTEXT
};
#if 0
struct Context {
enum {
H = 2448,
W = 3264
};
int cur_cmp;
int cur_jpeg_x;
int cur_jpeg_y;
ContextTypes annot;
int p[3][H/8][W/8][8][8][NUMCONTEXT][3];
};
extern Context *gctx;
#define ANNOTATION_ENABLED
#define ANNOTATE_CTX(bpos,annot_type,ctxnum,value) \
(gctx->annot = annot_type, \
gctx->p[gctx->cur_cmp][gctx->cur_jpeg_y][gctx->cur_jpeg_x][bpos/8][bpos%8][annot_type][ctxnum] = value)
#else
#define ANNOTATE_CTX(bpos, annot_type, ctxnum, value)
#endif
class Slice;
void optimize_model(Model&model);
void serialize_model(const Model & model, int output_fd);
void reset_model(Model &model);
void normalize_model(Model &model);
void load_model(Model &model, const char* filename);
#ifdef _WIN32
#define WINALIGN16 __declspec(align(16))
#define UNIXALIGN16
#else
#define WINALIGN16
#define UNIXALIGN16 __attribute__((aligned(16)))
#endif
class ProbabilityTablesBase {
protected:
Model model_;
static WINALIGN16 int32_t icos_idct_edge_8192_dequantized_x_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
static WINALIGN16 int32_t icos_idct_edge_8192_dequantized_y_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
static WINALIGN16 int32_t icos_idct_linear_8192_dequantized_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
static WINALIGN16 uint16_t quantization_table_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
static WINALIGN16 uint16_t freqmax_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
static WINALIGN16 uint8_t bitlen_freqmax_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
static WINALIGN16 uint8_t min_noise_threshold_[(int)ColorChannel::NumBlockTypes][64] UNIXALIGN16;
public:
Model &model() {return model_;}
void load_probability_tables();
static uint16_t* quantization_table(uint8_t color) {
return quantization_table_[color];
}
static uint16_t quantization_table(uint8_t color, uint8_t coef) {
return quantization_table_[color][coef];
}
static uint16_t freqmax(uint8_t color, uint8_t coef) {
return freqmax_[color][coef];
}
static uint8_t bitlen_freqmax(uint8_t color, uint8_t coef) {
return bitlen_freqmax_[color][coef];
}
static uint8_t min_noise_threshold(uint8_t color, uint8_t coef) {
return min_noise_threshold_[color][coef];
}
static void set_quantization_table(BlockType color, const unsigned short quantization_table[64]) {
for (int i = 0; i < 64; ++i) {
quantization_table_[(int)color][i] = quantization_table[zigzag[i]];
}
for (int pixel_row = 0; pixel_row < 8; ++pixel_row) {
for (int i = 0; i < 8; ++i) {
icos_idct_linear_8192_dequantized((int)color)[pixel_row * 8 + i] = icos_idct_linear_8192_scaled[pixel_row * 8 + i] * quantization_table_[(int)color][i];
icos_idct_edge_8192_dequantized_x((int)color)[pixel_row * 8 + i] = icos_base_8192_scaled[i * 8] * quantization_table_[(int)color][i * 8 + pixel_row];
icos_idct_edge_8192_dequantized_y((int)color)[pixel_row * 8 + i] = icos_base_8192_scaled[i * 8] * quantization_table_[(int)color][pixel_row * 8 + i];
}
}
static const unsigned short int freqmax[] =
{
1024, 931, 985, 968, 1020, 968, 1020, 1020,
932, 858, 884, 840, 932, 838, 854, 854,
985, 884, 871, 875, 985, 878, 871, 854,
967, 841, 876, 844, 967, 886, 870, 837,
1020, 932, 985, 967, 1020, 969, 1020, 1020,
969, 838, 878, 886, 969, 838, 969, 838,
1020, 854, 871, 870, 1010, 969, 1020, 1020,
1020, 854, 854, 838, 1020, 838, 1020, 838
};
for (int coord = 0; coord < 64; ++coord) {
freqmax_[(int)color][coord] = (freqmax[coord] + quantization_table_[(int)color][coord] - 1);
if (quantization_table_[(int)color][coord]) {
freqmax_[(int)color][coord] /= quantization_table_[(int)color][coord];
}
uint8_t max_len = uint16bit_length(freqmax_[(int)color][coord]);
bitlen_freqmax_[(int)color][coord] = max_len;
if (max_len > (int)RESIDUAL_NOISE_FLOOR) {
min_noise_threshold_[(int)color][coord] = max_len - RESIDUAL_NOISE_FLOOR;
}
}
}
static int32_t *icos_idct_edge_8192_dequantized_x(int color) {
return icos_idct_edge_8192_dequantized_x_[(int)color];
}
static int32_t *icos_idct_edge_8192_dequantized_y(int color) {
return icos_idct_edge_8192_dequantized_y_[(int)color];
}
static int32_t *icos_idct_linear_8192_dequantized(int color) {
return icos_idct_linear_8192_dequantized_[(int)color];
}
struct CoefficientContext {
int best_prior; //lakhani or aavrg depending on coefficient number
uint8_t num_nonzeros_bin; // num_nonzeros mapped into a bin
uint8_t bsr_best_prior;
};
enum {
VECTORIZE = ::VECTORIZE,
MICROVECTORIZE = ::MICROVECTORIZE
};
};
#define USE_TEMPLATIZED_COLOR
#ifdef USE_TEMPLATIZED_COLOR
#define TEMPLATE_ARG_COLOR0 BlockType::Y
#define TEMPLATE_ARG_COLOR1 BlockType::Cb
#define TEMPLATE_ARG_COLOR2 BlockType::Cr
#define TEMPLATE_ARG_COLOR3 BlockType::Ck
#else
#define TEMPLATE_ARG_COLOR0 BlockType::Y
#define TEMPLATE_ARG_COLOR1 BlockType::Y
#define TEMPLATE_ARG_COLOR2 BlockType::Y
#define TEMPLATE_ARG_COLOR3 BlockType::Y
#endif
template <bool all_present, BlockType
#ifdef USE_TEMPLATIZED_COLOR
color
#else
deprecated_color
#endif
>
class ProbabilityTables
{
private:
typedef ProbabilityTablesBase::CoefficientContext CoefficientContext;
const bool left_present;
const bool above_present;
const bool above_right_present;
public:
#ifdef USE_TEMPLATIZED_COLOR
enum {
COLOR = (int)color
};
ProbabilityTables(BlockType kcolor,
bool in_left_present,
bool in_above_present,
bool in_above_right_present)
: left_present(in_left_present),
above_present(in_above_present),
above_right_present(in_above_right_present) {
always_assert((left_present && above_present && above_right_present) == all_present);
always_assert(kcolor == color);
}
#else
const BlockType COLOR;
ProbabilityTables(BlockType color,
bool in_left_present,
bool in_above_present,
bool in_above_right_present)
: left_present(in_left_present),
above_present(in_above_present),
above_right_present(in_above_right_present),
COLOR(color) {
always_assert((left_present && right_present && above_right_present) == all_present);
static_assert((int)deprecated_color == 0, "Using dynamic color");
}
#endif
void reset(ProbabilityTablesBase&base) {
reset_model(base.model());
}
void load(ProbabilityTablesBase&base, const char * filename) {
load_model(base.model(), filename);
}
int color_index() {
if (BLOCK_TYPES == 2) {
if (0 == (int)COLOR) {
return 0;
}
return 1;
} else {
return std::min((int)(BLOCK_TYPES - 1), (int)COLOR);
}
}
ProbabilityTablesBase::CoefficientContext update_coefficient_context7x7(int coord,
int aligned_zz,
const ConstBlockContext block, uint8_t num_nonzeros_left) {
ProbabilityTablesBase::CoefficientContext retval;
retval.best_prior = compute_aavrg(coord, aligned_zz, block);
retval.num_nonzeros_bin = num_nonzeros_to_bin(num_nonzeros_left);
retval.bsr_best_prior = bit_length(std::min(abs(retval.best_prior), 1023));
return retval;
}
ProbabilityTablesBase::CoefficientContext update_coefficient_context7x7_precomp(int aligned_zz,
int aavrg,
const ConstBlockContext block, uint8_t num_nonzeros_left) {
ProbabilityTablesBase::CoefficientContext retval;
dev_assert(aavrg == compute_aavrg(aligned_to_raster.at(aligned_zz), aligned_zz, block));
//This was to make sure the code was right compute_aavrg_vec(aligned_zz, block);
retval.best_prior = aavrg;
retval.num_nonzeros_bin = num_nonzeros_to_bin(num_nonzeros_left);
retval.bsr_best_prior = bit_length(std::min(abs(retval.best_prior), 1023));
return retval;
}
ProbabilityTablesBase::CoefficientContext update_coefficient_context8(uint8_t coefficient,
const ConstBlockContext block, uint8_t num_nonzeros_x) {
CoefficientContext retval = {0, 0, 0};
#ifndef USE_SCALAR
if (MICROVECTORIZE) {
retval.best_prior = (coefficient & 7)
? compute_lak_horizontal(block, coefficient) : compute_lak_vertical(block, coefficient);
} else {
retval.best_prior = compute_lak(block, coefficient);
}
#else
retval.best_prior = compute_lak(block, coefficient);
#endif
retval.num_nonzeros_bin = num_nonzeros_x;
retval.bsr_best_prior = bit_length(std::min(abs(retval.best_prior), 1023));
return retval;
}
#ifndef USE_SCALAR
ProbabilityTablesBase::CoefficientContext update_coefficient_context8_horiz(uint8_t coefficient,
const ConstBlockContext block, uint8_t num_nonzeros_x) {
CoefficientContext retval = {0, 0, 0};
retval.best_prior = compute_lak_horizontal(block, coefficient);
retval.num_nonzeros_bin = num_nonzeros_x;
retval.bsr_best_prior = bit_length(std::min(abs(retval.best_prior), 1023));
return retval;
}
ProbabilityTablesBase::CoefficientContext update_coefficient_context8_vert(uint8_t coefficient,
const ConstBlockContext block, uint8_t num_nonzeros_x) {
CoefficientContext retval = {0, 0, 0};
retval.best_prior = compute_lak_vertical(block, coefficient);
retval.num_nonzeros_bin = num_nonzeros_x;
retval.bsr_best_prior = bit_length(std::min(abs(retval.best_prior), 1023));
return retval;
}
#define INSTANTIATE_TEMPLATE_METHOD(N) \
ProbabilityTablesBase::CoefficientContext update_coefficient_context8_templ##N(const ConstBlockContext block, \
uint8_t num_nonzeros_x) { \
ProbabilityTablesBase::CoefficientContext retval = {0, 0, 0}; \
retval.best_prior = compute_lak_templ<N>(block); \
retval.num_nonzeros_bin = num_nonzeros_x; \
retval.bsr_best_prior = bit_length(std::min(abs(retval.best_prior), 1023)); \
return retval; \
}
INSTANTIATE_TEMPLATE_METHOD(1)
INSTANTIATE_TEMPLATE_METHOD(2)
INSTANTIATE_TEMPLATE_METHOD(3)
INSTANTIATE_TEMPLATE_METHOD(4)
INSTANTIATE_TEMPLATE_METHOD(5)
INSTANTIATE_TEMPLATE_METHOD(6)
INSTANTIATE_TEMPLATE_METHOD(7)
INSTANTIATE_TEMPLATE_METHOD(8)
INSTANTIATE_TEMPLATE_METHOD(16)
INSTANTIATE_TEMPLATE_METHOD(24)
INSTANTIATE_TEMPLATE_METHOD(32)
INSTANTIATE_TEMPLATE_METHOD(40)
INSTANTIATE_TEMPLATE_METHOD(48)
INSTANTIATE_TEMPLATE_METHOD(56)
#endif
Sirikata::Array2d<Branch, 6, 32>::Slice nonzero_counts_7x7(ProbabilityTablesBase &pt,
const ConstBlockContext block) {
uint8_t num_nonzeros_above = 0;
uint8_t num_nonzeros_left = 0;
if (all_present || above_present) {
num_nonzeros_above = block.nonzeros_above_7x7_unchecked();
}
if (all_present || left_present) {
num_nonzeros_left = block.nonzeros_left_7x7_unchecked();
}
uint8_t num_nonzeros_context = 0;
if ((!all_present) && above_present && !left_present) {
num_nonzeros_context = (num_nonzeros_above + 1) / 2;
} else if ((!all_present) && left_present && !above_present) {
num_nonzeros_context = (num_nonzeros_left + 1) / 2;
} else if (all_present || (left_present && above_present)) {
num_nonzeros_context = (num_nonzeros_above + num_nonzeros_left + 2) / 4;
}
ANNOTATE_CTX(0, ZEROS7x7, 0, num_nonzeros_context);
return pt.model().num_nonzeros_counts_7x7_.at(color_index(),
num_nonzeros_to_bin(num_nonzeros_context));
}
Sirikata::Array2d<Branch, 3u, 4u>::Slice x_nonzero_counts_8x1(ProbabilityTablesBase &pt,
unsigned int eob_x,
unsigned int num_nonzeros) {
ANNOTATE_CTX(0, ZEROS8x1, 0, ((num_nonzeros + 3) / 7));
ANNOTATE_CTX(0, ZEROS8x1, 1, eob_x);
return pt.model().num_nonzeros_counts_8x1_.at(color_index(), eob_x, ((num_nonzeros + 3) / 7));
}
Sirikata::Array2d<Branch, 3u, 4u>::Slice y_nonzero_counts_1x8(ProbabilityTablesBase &pt,
unsigned int eob_x,
unsigned int num_nonzeros) {
ANNOTATE_CTX(0, ZEROS1x8, 0, ((num_nonzeros + 3) / 7));
ANNOTATE_CTX(0, ZEROS1x8, 1, eob_x);
return pt.model().num_nonzeros_counts_1x8_.at(color_index(), eob_x, ((num_nonzeros + 3) / 7));
}
Sirikata::Array1d<Branch, MAX_EXPONENT>::Slice exponent_array_x(ProbabilityTablesBase &pt, int band, int zig15, CoefficientContext context) {
ANNOTATE_CTX(band, EXP8, 0, context.bsr_best_prior);
ANNOTATE_CTX(band, EXP8, 1, context.num_nonzeros);
dev_assert((band & 7)== 0 ? ((band >>3) + 7) : band - 1 == zig15);
return pt.model().exponent_counts_x_.at(color_index(),
context.num_nonzeros_bin,
zig15,
context.bsr_best_prior);
}
Sirikata::Array1d<Branch, MAX_EXPONENT>::Slice exponent_array_7x7(ProbabilityTablesBase &pt,
const unsigned int band,
const unsigned int zig49,
const CoefficientContext context) {
ANNOTATE_CTX(band, EXP7x7, 0, context.bsr_best_prior);
ANNOTATE_CTX(band, EXP7x7, 1, context.num_nonzeros_bin);
return pt.model().exponent_counts_.at(color_index(),
context.num_nonzeros_bin,
zig49,
context.bsr_best_prior);
}
Sirikata::Array1d<Branch,
MAX_EXPONENT>::Slice exponent_array_dc(ProbabilityTablesBase &pt,
uint16_t len_abs_mxm,
uint16_t len_abs_offset_to_closest_edge) {
return pt.model().exponent_counts_dc_.
at(std::min(len_abs_mxm,
(uint16_t)(Model::ExponentCountsDC::size0 - 1)),
std::min(len_abs_offset_to_closest_edge,
(uint16_t)(Model::ExponentCountsDC::size1 - 1)));
}
Sirikata::Array1d<Branch, COEF_BITS>::Slice residual_array_dc(ProbabilityTablesBase &pt,
uint16_t len_abs_mxm
, uint16_t len_abs_offset_to_closest_edge) {
return pt.model().residual_noise_counts_dc_
.at(std::min((uint16_t)(Model::ResidualNoiseCountsDc::size0 - 1),
len_abs_mxm));
}
Sirikata::Array1d<Branch, COEF_BITS>::Slice residual_noise_array_x(ProbabilityTablesBase &pt,
const unsigned int band,
const CoefficientContext context) {
ANNOTATE_CTX(band, RES8, 0, num_nonzeros_x);
return residual_noise_array_shared(pt, band,
context);
}
Sirikata::Array1d<Branch, COEF_BITS>::Slice residual_noise_array_shared(ProbabilityTablesBase &pt,
const unsigned int band,
const CoefficientContext context) {
return pt.model().residual_noise_counts_.at(color_index(),
band/band_divisor,
context.num_nonzeros_bin);
}
Sirikata::Array1d<Branch, COEF_BITS>::Slice residual_noise_array_7x7(ProbabilityTablesBase &pt,
const unsigned int band,
const CoefficientContext context) {
if (band == 0) {
ANNOTATE_CTX(0, RESDC, 0, num_nonzeros_to_bin(num_nonzeros));
} else {
ANNOTATE_CTX(band, RES7x7, 0, num_nonzeros_to_bin(num_nonzeros));
}
return residual_noise_array_shared(pt, band, context);
}
unsigned int num_nonzeros_to_bin(uint8_t num_nonzeros) {
return nonzero_to_bin[NUM_NONZEROS_BINS-1][num_nonzeros];
}
int idct_2d_8x1(const AlignedBlock&block, bool ignore_first, int pixel_row) {
int retval = 0;
if (!ignore_first) {
retval = block.coefficients_raster(0) * ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 0];
}
retval += block.coefficients_raster(1)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 1];
retval += block.coefficients_raster(2)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 2];
retval += block.coefficients_raster(3)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 3];
retval += block.coefficients_raster(4)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 4];
retval += block.coefficients_raster(5)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 5];
retval += block.coefficients_raster(6)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 6];
retval += block.coefficients_raster(7)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 7];
return retval;
}
int idct_2d_1x8(const AlignedBlock&block, bool ignore_first, int pixel_row) {
int retval = 0;
if (!ignore_first) {
retval = block.dc() * ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 0];
}
retval += block.coefficients_raster(8)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 1];
retval += block.coefficients_raster(16)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 2];
retval += block.coefficients_raster(24)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 3];
retval += block.coefficients_raster(32)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 4];
retval += block.coefficients_raster(40)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 5];
retval += block.coefficients_raster(48)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 6];
retval += block.coefficients_raster(56)
* ProbabilityTablesBase::icos_idct_linear_8192_dequantized((int)COLOR)[pixel_row * 8 + 7];
return retval;
}
int predict_dc_dct(const ConstBlockContext&context) {
int prediction = 0;
int left_block = 0;
int left_edge = 0;
int above_block = 0;
int above_edge = 0;
if (all_present || left_present) {
left_block = idct_2d_8x1(context.left_unchecked(), 0, 7);
left_edge = idct_2d_8x1(context.here(), 1, 0);
}
if (all_present || above_present) {
above_block = idct_2d_1x8(context.above_unchecked(), 0, 7);
above_edge = idct_2d_1x8(context.here(), 1, 0);
}
if (all_present || left_present) {
if (all_present || above_present) {
prediction = ( ( left_block - left_edge ) + (above_block - above_edge) ) * 4;
} else {
prediction = ( left_block - left_edge ) * 8;
}
} else if (above_present) {
prediction = ( above_block - above_edge ) * 8;
}
int DCT_RSC = 8192;
prediction = std::max(-1024 * DCT_RSC, std::min(1016 * DCT_RSC, prediction));
prediction /= ProbabilityTablesBase::quantization_table((int)COLOR, 0);
int round = DCT_RSC/2;
if (prediction < 0) {
round = -round;
}
return (prediction + round) / DCT_RSC;
}
int predict_locoi_dc_deprecated(const ConstBlockContext&context) {
if (all_present || left_present) {
int a = context.left_unchecked().dc();
if (all_present || above_present) {
int b = context.above_unchecked().dc();
int c = context.above_left_unchecked().dc();
if (c >= std::max(a,b)) {
return std::min(a,b);
} else if (c <= std::min(a,b)) {
return std::max(a,b);
}
return a + b - c;
}else {
return a;
}
} else if (above_present) {
return context.above_unchecked().dc();
} else {
return 0;
}
}
int predict_or_unpredict_dc(const ConstBlockContext&context, bool recover_original) {
int max_value = (1 << (1 + MAX_EXPONENT)) - 1;
int min_value = -max_value;
int adjustment_factor = 2 * max_value + 1;
int retval = //predict_locoi_dc_deprecated(block);
predict_dc_dct(context);
retval = context.here().dc() + (recover_original ? retval : -retval);
if (retval < min_value) retval += adjustment_factor;
if (retval > max_value) retval -= adjustment_factor;
return retval;
}
#define shift_right_round_zero_epi16(vec, imm8) (_mm_sign_epi16(_mm_srli_epi16(_mm_sign_epi16(vec, vec), imm8), vec));
int adv_predict_dc_pix(const ConstBlockContext&context, int16_t*pixels_sans_dc, int32_t *uncertainty_val, int32_t *uncertainty2_val) {
uint16_t *q = ProbabilityTablesBase::quantization_table((int)color);
idct(context.here(), q, pixels_sans_dc, true);
Sirikata::AlignedArray1d<int16_t, 16> dc_estimates;
dc_estimates.memset(0);
int32_t avgmed = 0;
if(all_present || left_present || above_present) {
#ifndef USE_SCALAR
if (all_present || above_present) { //above goes first to prime the cache
__m128i neighbor_above = _mm_loadu_si128((const __m128i*)(const char*)context
.neighbor_context_above_unchecked()
.horizontal_ptr());
__m128i pixels_sans_dc_reg = _mm_loadu_si128((const __m128i*)(const char*)pixels_sans_dc);
__m128i pixels2_sans_dc_reg = _mm_loadu_si128((const __m128i*)(const char*)(pixels_sans_dc + 8));
__m128i pixels_delta = _mm_sub_epi16(pixels_sans_dc_reg,
pixels2_sans_dc_reg);
__m128i pixels_delta_div2 = shift_right_round_zero_epi16(pixels_delta, 1);
__m128i pixels_sans_dc_recentered = _mm_add_epi16(pixels_sans_dc_reg,
_mm_set1_epi16(1024));
__m128i above_dc_estimate = _mm_sub_epi16(_mm_sub_epi16(neighbor_above, pixels_delta_div2),
pixels_sans_dc_recentered);
_mm_store_si128((__m128i*)(char*)(dc_estimates.begin()
+ ((all_present || left_present) ? 8 : 0)),
above_dc_estimate);
}
if (all_present || left_present) {
const int16_t * horiz_data = context.neighbor_context_left_unchecked().vertical_ptr_except_7();
__m128i neighbor_horiz = _mm_loadu_si128((const __m128i*)(const char*)horiz_data);
//neighbor_horiz = _mm_insert_epi16(neighbor_horiz, horiz_data[NeighborSummary::VERTICAL_LAST_PIXEL_OFFSET_FROM_FIRST_PIXEL], 7);
__m128i pixels_sans_dc_reg = _mm_set_epi16(pixels_sans_dc[56],
pixels_sans_dc[48],
pixels_sans_dc[40],
pixels_sans_dc[32],
pixels_sans_dc[24],
pixels_sans_dc[16],
pixels_sans_dc[8],
pixels_sans_dc[0]);
__m128i pixels_delta = _mm_sub_epi16(pixels_sans_dc_reg,
_mm_set_epi16(pixels_sans_dc[57],
pixels_sans_dc[49],
pixels_sans_dc[41],
pixels_sans_dc[33],
pixels_sans_dc[25],
pixels_sans_dc[17],
pixels_sans_dc[9],
pixels_sans_dc[1]));
__m128i pixels_delta_div2 = shift_right_round_zero_epi16(pixels_delta, 1);
__m128i left_dc_estimate = _mm_sub_epi16(_mm_sub_epi16(neighbor_horiz, pixels_delta_div2),
_mm_add_epi16(pixels_sans_dc_reg,
_mm_set1_epi16(1024)));
_mm_store_si128((__m128i*)(char*)dc_estimates.begin(), left_dc_estimate);
}
#else
if (all_present || left_present) {
for (int i = 0; i < 8;++i) {
int a = pixels_sans_dc[i << 3] + 1024;
int pixel_delta = pixels_sans_dc[i << 3] - pixels_sans_dc[(i << 3) + 1];
int b = context.neighbor_context_left_unchecked().vertical(i) - (pixel_delta / 2); //round to zero
dc_estimates[i] = b - a;
}
}
if (all_present || above_present) {
for (int i = 0; i < 8;++i) {
int a = pixels_sans_dc[i] + 1024;
int pixel_delta = pixels_sans_dc[i] - pixels_sans_dc[i + 8];
int b = context.neighbor_context_above_unchecked().horizontal(i) - (pixel_delta / 2); //round to zero
dc_estimates[i + ((all_present || left_present) ? 8 : 0)] = b - a;
}
}
#endif
int32_t avg_h_v[2] = {0, 0};
int32_t min_dc = dc_estimates[0];
int32_t max_dc = dc_estimates[0];
size_t which_est = 0;
for (int vert = 0; vert != 2; ++vert) {
for (int i = 0; i < 8; ++which_est, ++i) {
int16_t cur_est = dc_estimates[which_est];
avg_h_v[vert] += cur_est;
if (min_dc > cur_est) {
min_dc = cur_est;
}
if (max_dc < cur_est) {
max_dc = cur_est;
}
}
if ((!all_present) && (above_present == false || left_present == false)) {
avg_h_v[1] = avg_h_v[0];
break;
}
}
int32_t overall_avg = (avg_h_v[0] + avg_h_v[1]) >> 1;
avgmed = overall_avg;
*uncertainty_val = (max_dc - min_dc)>>3;
avg_h_v[0] -= avgmed;
avg_h_v[1] -= avgmed;
int32_t far_afield_value = avg_h_v[1];
if (abs(avg_h_v[0]) < abs(avg_h_v[1])) {
far_afield_value = avg_h_v[0];
}
*uncertainty2_val = (far_afield_value) >> 3;
if (false) { // this is to debug some of the differences
debug_print_deltas(context, dc_estimates.begin(), avgmed);
}
}
return ((avgmed / q[0] + 4) >> 3);
}
void debug_print_deltas(const ConstBlockContext&context, int16_t *dc_estimates, int avgmed) {
int actual_dc = context.here().dc();
uint16_t *q = ProbabilityTablesBase::quantization_table((int)color);
int len_est = ((all_present || (left_present && above_present)) ? 16 : 8);
int avg_estimated_dc = 0;
int dc_sum = 0;
for (int i = 0 ;i < len_est; ++i) {
dc_sum += dc_estimates[i];
}
avg_estimated_dc = dc_sum;
if (all_present || (left_present && above_present)) {
avg_estimated_dc >>= 1;
}
avg_estimated_dc = (avg_estimated_dc/q[0] + xIDCTSCALE / 2) >> 3;
int16_t dc_copy[16];
memcpy(dc_copy, dc_estimates, len_est*sizeof(int16_t));
std::sort(dc_copy, dc_copy + len_est);
int mmed = dc_copy[len_est/2];
int scaled_med = (mmed/q[0] + 4);
int scaled_avgmed = (((avgmed/q[0]) + 4) >> 3);
using namespace LeptonDebug;
LeptonDebug::med_err += abs(scaled_med - actual_dc);
LeptonDebug::amd_err += abs(scaled_avgmed - actual_dc);
LeptonDebug::avg_err += abs(avg_estimated_dc - actual_dc);
int locoi_pred = predict_locoi_dc_deprecated(context);
int predicted_dc = predict_dc_dct(context);
LeptonDebug::ori_err += abs(predicted_dc - actual_dc);
LeptonDebug::loc_err += abs(locoi_pred - actual_dc);
fprintf(stderr, "MXM: %d\n", dc_estimates[len_est - 1] - dc_estimates[0]);
fprintf(stderr, "MED: %d (%d)\n", scaled_med, LeptonDebug::med_err);
fprintf(stderr, "AMD: %d (%d)\n", scaled_avgmed, LeptonDebug::amd_err);
fprintf(stderr, "AVG: %d (%d)\n", avg_estimated_dc, LeptonDebug::avg_err);
fprintf(stderr, "ORI: %d (%d)\n", predicted_dc, LeptonDebug::ori_err);
fprintf(stderr, "LOC: %d (%d)\n", locoi_pred, LeptonDebug::loc_err);
fprintf(stderr, "DC : %d\n", actual_dc);
}
int adv_predict_or_unpredict_dc(int16_t saved_dc, bool recover_original, int predicted_val) {
int max_value = (1 << (MAX_EXPONENT - 1));
int min_value = -max_value;
int adjustment_factor = 2 * max_value + 1;
int retval = predicted_val;
retval = saved_dc + (recover_original ? retval : -retval);
if (retval < min_value) retval += adjustment_factor;
if (retval > max_value) retval -= adjustment_factor;
return retval;
}
int compute_aavrg_dc(ConstBlockContext context) {
return compute_aavrg(0, raster_to_aligned.at(0), context);
uint32_t total = 0;
if (all_present || left_present) {
total += abs(context.left_unchecked().dc());
}
if (all_present || above_present) {
total += abs(context.above_unchecked().dc());
}
if (all_present || (left_present && above_present)) {
constexpr unsigned int log_weight = 5;
total *= 13;
total += 6 * abs(context.above_left_unchecked().dc());
return total >> log_weight;
} else {
return total;
}
}
int16_t compute_aavrg(unsigned int coord, unsigned int aligned_zz, ConstBlockContext context) {
int16_t total = 0;
if (all_present || left_present) {
total += abs(context.left_unchecked().coefficients_raster(coord));
}
if (all_present || above_present) {
total += abs(context.above_unchecked().coefficients_raster(coord));
}
if (all_present || (left_present && above_present)) {
constexpr unsigned int log_weight = 5;
total *= 13;
total += 6 * abs(context.above_left_unchecked().coefficients_raster(coord));
return ((uint16_t)total) >> log_weight;
} else {
return total;
}
//if (block.context().above_right.initialized()) {
//total += abs(block.context().above_right.get()->coefficients().at(0));
//}
}
#if defined(OPTIMIZED_7x7) && !defined(USE_SCALAR)
bool aavrg_vec_matches(__m128i retval, unsigned int aligned_zz, ConstBlockContext context) {
short ret[8];
_mm_storeu_si128((__m128i*)(char*)ret, retval);
short correct[8] = {compute_aavrg(aligned_to_raster.at(aligned_zz), aligned_zz +0, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+1), aligned_zz + 1, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+2), aligned_zz + 2, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+3), aligned_zz + 3, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+4), aligned_zz + 4, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+5), aligned_zz + 5, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+6), aligned_zz + 6, context),
compute_aavrg(aligned_to_raster.at(aligned_zz+7), aligned_zz + 7, context)};
return (memcmp(ret, correct, sizeof(correct)) == 0);
}
void compute_aavrg_vec(unsigned int aligned_zz, ConstBlockContext context, short* aligned_retval) {
_mm_store_si128((__m128i*)(char*)aligned_retval, compute_aavrg_vec(aligned_zz, context));
}
#if defined (__clang__) || defined(__GNUC__)
#define x_mm_loadu_si64(a) _mm_set1_epi64x(*(uint64_t*)(char*)(a))
#else
#define x_mm_loadu_si64 _mm_loadu_si64
#endif
__m128i compute_aavrg_vec(unsigned int aligned_zz, ConstBlockContext context) {
if (all_present == false && left_present == false && above_present == false) {
return _mm_setzero_si128();
}
__m128i left;
if (all_present || left_present) {
left = _mm_abs_epi16(_mm_load_si128((const __m128i*)(const char*)&context.left_unchecked().coef.at(aligned_zz)));
if ((!all_present) && !above_present) {
return left;
}
}
__m128i above = _mm_setzero_si128();
if (all_present || above_present) {
above = _mm_abs_epi16(_mm_load_si128((const __m128i*)(const char*)&context.above_unchecked().coef.at(aligned_zz)));
if (all_present == false && !left_present) {
return above;
}
}
constexpr unsigned int log_weight = 5;
__m128i total = _mm_add_epi16(left, above);
total = _mm_mullo_epi16(total, _mm_set1_epi16(13)); // approximate (a*2+b*2 + c)/5 as (a *13 + b * 13 + c * 6)/32
__m128i aboveleft = _mm_abs_epi16(_mm_load_si128((const __m128i*)(const char*)&context.above_left_unchecked().coef.at(aligned_zz)));
total = _mm_add_epi16(total, _mm_mullo_epi16(aboveleft, _mm_set1_epi16(6)));
__m128i retval = _mm_srli_epi16(total, log_weight);
dev_assert(aavrg_vec_matches(retval, aligned_zz, context));
return retval;
//if (block.context().above_right.initialized()) {
//total += abs(block.context().above_right.get()->coefficients().at(0));
//}
}
#endif
#ifndef USE_SCALAR
static int32_t compute_lak_vec(__m128i coeffs_x_low, __m128i coeffs_x_high, __m128i coeffs_a_low, __m128i
#ifdef _WIN32
&
#endif
indirect_coeffs_a_high, const int32_t *icos_deq) {
__m128i sign_mask = _mm_set_epi32(-1, 1, -1, 1); // ((i & 1) ? -1 : 1)
//coeffs_x[i] = ((i & 1) ? -1 : 1) * coeffs_a[i] - coeffs_x[i];
coeffs_a_low = _mm_sign_epi32(coeffs_a_low, sign_mask);
__m128i coeffs_a_high = _mm_sign_epi32(indirect_coeffs_a_high, sign_mask);
coeffs_x_low = _mm_sub_epi32(coeffs_a_low, coeffs_x_low);
coeffs_x_high = _mm_sub_epi32(coeffs_a_high, coeffs_x_high);
__m128i icos_low = _mm_load_si128((const __m128i*)(const char*)icos_deq);
__m128i icos_high = _mm_load_si128((const __m128i*)(const char*)(icos_deq + 4));
// coeffs_x[i] *= icos[i]
__m128i deq_low = _mm_mullo_epi32(coeffs_x_low, icos_low);
__m128i deq_high = _mm_mullo_epi32(coeffs_x_high, icos_high);
__m128i sum = _mm_add_epi32(deq_low, deq_high);
sum = _mm_add_epi32(sum, _mm_srli_si128(sum, 8));
sum = _mm_add_epi32(sum, _mm_srli_si128(sum, 4));
// coeffs_x[0] = sum(coeffs_x)
int32_t prediction = _mm_cvtsi128_si32(sum);
//if (prediction > 0) { <-- rounding hurts prediction perf and costs compute this rounding didn't round the same way as the unvectorized one anyhow
// prediction += icos_deq[0]/2;
//} else {
// prediction -= icos_deq[0]/2; // round away from zero
//}
return prediction / icos_deq[0];
}
#define ITER(x_var, a_var, i, step) \
(x_var = _mm_set_epi32( context.here().coefficients_raster(band + step * ((i) + 3)), \
context.here().coefficients_raster(band + step * ((i) + 2)), \
context.here().coefficients_raster(band + step * ((i) + 1)), \
i == 0 ? 0 : context.here().coefficients_raster(band + step * (i))), \
a_var = _mm_set_epi32(neighbor.coefficients_raster(band + step * ((i) + 3)), \
neighbor.coefficients_raster(band + step * ((i) + 2)), \
neighbor.coefficients_raster(band + step * ((i) + 1)), \
neighbor.coefficients_raster(band + step * (i))))
template<int band>
#ifndef _WIN32
__attribute__((always_inline))
#endif
int32_t compute_lak_templ(const ConstBlockContext&context) {
__m128i coeffs_x_low;
__m128i coeffs_x_high;
__m128i coeffs_a_low;
__m128i coeffs_a_high;
const int32_t * icos = nullptr;
static_assert((band & 7) == 0 || (band >> 3) == 0, "This function only works on edges");
if ((band >> 3) == 0) {
if(all_present == false && !above_present) {
return 0;
}
const auto &neighbor = context.above_unchecked();
ITER(coeffs_x_low, coeffs_a_low, 0, 8);
ITER(coeffs_x_high, coeffs_a_high, 4, 8);
icos = ProbabilityTablesBase::icos_idct_edge_8192_dequantized_x((int)COLOR) + band * 8;
} else {
if (all_present == false && !left_present) {
return 0;
}
const auto &neighbor = context.left_unchecked();
ITER(coeffs_x_low, coeffs_a_low, 0, 1);
ITER(coeffs_x_high, coeffs_a_high, 4, 1);
icos = ProbabilityTablesBase::icos_idct_edge_8192_dequantized_y((int)COLOR) + band;
}
return compute_lak_vec(coeffs_x_low, coeffs_x_high, coeffs_a_low, coeffs_a_high, icos);
}
int32_t compute_lak_horizontal(const ConstBlockContext&context, unsigned int band) {
if (all_present == false && !above_present) {
return 0;
}
__m128i coeffs_x_low;
__m128i coeffs_x_high;
__m128i coeffs_a_low;
__m128i coeffs_a_high;
dev_assert(band/8 == 0 && "this function only works for the top edge");
const auto &neighbor = context.above_unchecked();
ITER(coeffs_x_low, coeffs_a_low, 0, 8);
ITER(coeffs_x_high, coeffs_a_high, 4, 8);
const int32_t * icos = ProbabilityTablesBase::icos_idct_edge_8192_dequantized_x((int)COLOR) + band * 8;
return compute_lak_vec(coeffs_x_low, coeffs_x_high, coeffs_a_low, coeffs_a_high, icos);
}
int32_t compute_lak_vertical(const ConstBlockContext&context, unsigned int band) {
dev_assert((band & 7) == 0 && "Must be used for veritcal");
if (all_present == false && !left_present) {
return 0;
}
__m128i coeffs_x_low;
__m128i coeffs_x_high;
__m128i coeffs_a_low;
__m128i coeffs_a_high;