--- /dev/null
+use nihav_core::io::byteio::*;
+use nihav_core::codecs::{EncoderResult, EncoderError};
+use nihav_codec_support::codecs::MV;
+use super::super::vpcommon::*;
+use super::super::vp6data::*;
+use super::models::*;
+
+struct EncSeq {
+ bit: bool,
+ idx: u8,
+}
+
+pub struct TokenSeq<T: PartialEq> {
+ val: T,
+ seq: &'static [EncSeq],
+}
+
+macro_rules! bit_entry {
+ (T; $idx:expr) => {EncSeq {bit: true, idx: $idx }};
+ (F; $idx:expr) => {EncSeq {bit: false, idx: $idx }};
+}
+
+macro_rules! bit_seq {
+ ($val: expr; $( $bit:tt),* ; $( $idx:expr),* ) => {
+ TokenSeq {
+ val: $val,
+ seq:
+ &[
+ $(
+ bit_entry!($bit; $idx),
+ )*
+ ]
+ }
+ };
+}
+
+pub const MODE_TREE: &[TokenSeq<VPMBType>] = &[
+ bit_seq!(VPMBType::Intra; T, F, F; 0, 2, 5),
+ bit_seq!(VPMBType::InterFourMV; T, F, T; 0, 2, 5),
+ bit_seq!(VPMBType::InterNoMV; F, F, F; 0, 1, 3),
+ bit_seq!(VPMBType::InterMV; F, F, T; 0, 1, 3),
+ bit_seq!(VPMBType::InterNearest; F, T, F; 0, 1, 4),
+ bit_seq!(VPMBType::InterNear; F, T, T; 0, 1, 4),
+ bit_seq!(VPMBType::GoldenNoMV; T, T, F, F; 0, 2, 6, 7),
+ bit_seq!(VPMBType::GoldenMV; T, T, F, T; 0, 2, 6, 7),
+ bit_seq!(VPMBType::GoldenNearest; T, T, T, F; 0, 2, 6, 8),
+ bit_seq!(VPMBType::GoldenNear; T, T, T, T; 0, 2, 6, 8),
+];
+
+const MODE_TREE_DIFF: &[TokenSeq<u8>] = &[
+ bit_seq!(1; F, T; 0, 1),
+ bit_seq!(2; F, F; 0, 1),
+ bit_seq!(3; T, F, T; 0, 2, 3),
+ bit_seq!(4; T, F, F, T; 0, 2, 3, 4),
+ bit_seq!(5; T, F, F, F, T; 0, 2, 3, 4, 5),
+ bit_seq!(6; T, F, F, F, F; 0, 2, 3, 4, 5),
+ bit_seq!(7; T, T; 0, 2),
+];
+
+const MODE_TREE_DIFF_PROBS: &[u8; 6] = &[171, 83, 199, 140, 125, 104];
+
+const SHORT_MV_TREE: &[TokenSeq<u8>] = &[
+ bit_seq!(0; F, F, F; 0, 1, 2),
+ bit_seq!(1; F, F, T; 0, 1, 2),
+ bit_seq!(2; F, T, F; 0, 1, 3),
+ bit_seq!(3; F, T, T; 0, 1, 3),
+ bit_seq!(4; T, F, F; 0, 4, 5),
+ bit_seq!(5; T, F, T; 0, 4, 5),
+ bit_seq!(6; T, T, F; 0, 4, 6),
+ bit_seq!(7; T, T, T; 0, 4, 6),
+];
+
+const EOB: i8 = 42;
+
+const DC_TREE: &[TokenSeq<i8>] = &[
+ bit_seq!( 0; F; 0),
+ bit_seq!( 1; T, F; 0, 2),
+ bit_seq!( 2; T, T, F, F; 0, 2, 3, 4),
+ bit_seq!( 3; T, T, F, T, F; 0, 2, 3, 4, 5),
+ bit_seq!( 4; T, T, F, T, T; 0, 2, 3, 4, 5),
+ bit_seq!( -1; T, T, T, F, F; 0, 2, 3, 6, 7),
+ bit_seq!( -2; T, T, T, F, T; 0, 2, 3, 6, 7),
+ bit_seq!( -3; T, T, T, T, F, F; 0, 2, 3, 6, 8, 9),
+ bit_seq!( -4; T, T, T, T, F, T; 0, 2, 3, 6, 8, 9),
+ bit_seq!( -5; T, T, T, T, T, F; 0, 2, 3, 6, 8, 10),
+ bit_seq!( -6; T, T, T, T, T, T; 0, 2, 3, 6, 8, 10),
+];
+
+const NZ_COEF_TREE: &[TokenSeq<i8>] = &[
+ bit_seq!( 1; F; 2),
+ bit_seq!( 2; T, F, F; 2, 3, 4),
+ bit_seq!( 3; T, F, T, F; 2, 3, 4, 5),
+ bit_seq!( 4; T, F, T, T; 2, 3, 4, 5),
+ bit_seq!( -1; T, T, F, F; 2, 3, 6, 7),
+ bit_seq!( -2; T, T, F, T; 2, 3, 6, 7),
+ bit_seq!( -3; T, T, T, F, F; 2, 3, 6, 8, 9),
+ bit_seq!( -4; T, T, T, F, T; 2, 3, 6, 8, 9),
+ bit_seq!( -5; T, T, T, T, F; 2, 3, 6, 8, 10),
+ bit_seq!( -6; T, T, T, T, T; 2, 3, 6, 8, 10),
+];
+
+const COEF_TREE: &[TokenSeq<i8>] = &[
+ bit_seq!( 0; F, T; 0, 1),
+ bit_seq!(EOB; F, F; 0, 1),
+ bit_seq!( 1; T, F; 0, 2),
+ bit_seq!( 2; T, T, F, F; 0, 2, 3, 4),
+ bit_seq!( 3; T, T, F, T, F; 0, 2, 3, 4, 5),
+ bit_seq!( 4; T, T, F, T, T; 0, 2, 3, 4, 5),
+ bit_seq!( -1; T, T, T, F, F; 0, 2, 3, 6, 7),
+ bit_seq!( -2; T, T, T, F, T; 0, 2, 3, 6, 7),
+ bit_seq!( -3; T, T, T, T, F, F; 0, 2, 3, 6, 8, 9),
+ bit_seq!( -4; T, T, T, T, F, T; 0, 2, 3, 6, 8, 9),
+ bit_seq!( -5; T, T, T, T, T, F; 0, 2, 3, 6, 8, 10),
+ bit_seq!( -6; T, T, T, T, T, T; 0, 2, 3, 6, 8, 10),
+];
+
+fn coef_to_cat(coef: i16) -> i8 {
+ match coef.abs() {
+ 0 ..=4 => coef.abs() as i8,
+ 5 ..=6 => -1,
+ 7 ..=10 => -2,
+ 11..=18 => -3,
+ 19..=34 => -4,
+ 35..=66 => -5,
+ _ => -6,
+ }
+}
+
+const ZERO_RUN_TREE: &[TokenSeq<u8>] = &[
+ bit_seq!(1; F, F, F; 0, 1, 2),
+ bit_seq!(2; F, F, T; 0, 1, 2),
+ bit_seq!(3; F, T, F; 0, 1, 3),
+ bit_seq!(4; F, T, T; 0, 1, 3),
+ bit_seq!(5; T, F, F, F; 0, 4, 5, 6),
+ bit_seq!(6; T, F, F, T; 0, 4, 5, 6),
+ bit_seq!(7; T, F, T, F; 0, 4, 5, 7),
+ bit_seq!(8; T, F, T, T; 0, 4, 5, 7),
+ bit_seq!(9; T, T; 0, 4),
+];
+
+pub struct BoolEncoder<'a, 'b> {
+ bw: &'a mut ByteWriter<'b>,
+ val: u32,
+ range: u32,
+ bits: u8,
+ saved: u8,
+ run: usize,
+}
+
+impl<'a, 'b> BoolEncoder<'a, 'b> {
+ pub fn new(bw: &'a mut ByteWriter<'b>) -> Self {
+ Self {
+ bw,
+ val: 0,
+ range: 255,
+ bits: 0,
+ saved: 0,
+ run: 0,
+ }
+ }
+ pub fn put_bool(&mut self, bit: bool, prob: u8) -> EncoderResult<()> {
+ let split = 1 + (((self.range - 1) * u32::from(prob)) >> 8);
+ if bit {
+ self.range -= split;
+ self.val += split;
+ } else {
+ self.range = split;
+ }
+
+ if self.range < 128 {
+ self.renorm()?;
+ }
+ Ok(())
+ }
+ fn flush_run(&mut self, overflow: bool) -> EncoderResult<()> {
+ if self.run > 0 {
+ self.bw.write_byte(self.saved + (overflow as u8))?;
+ if !overflow {
+ for _ in 1..self.run {
+ self.bw.write_byte(0xFF)?;
+ }
+ } else {
+ for _ in 1..self.run {
+ self.bw.write_byte(0)?;
+ }
+ }
+ self.run = 0;
+ }
+ Ok(())
+ }
+ fn renorm(&mut self) -> EncoderResult<()> {
+ let bits = (self.range.leading_zeros() & 7) as u8;
+ self.range <<= bits;
+ if self.bits + bits < 23 {
+ self.bits += bits;
+ self.val <<= bits;
+ } else {
+ for _ in 0..bits {
+ if (self.bits == 23) && ((self.val >> 31) != 0) {
+ self.flush_run(true)?;
+ }
+ self.val <<= 1;
+ self.bits += 1;
+ if self.bits == 24 {
+ let tbyte = (self.val >> 24) as u8;
+ let nbyte = (self.val >> 16) as u8;
+ if tbyte < 0xFF {
+ self.flush_run(false)?;
+ if nbyte < 0xFE {
+ self.bw.write_byte(tbyte)?;
+ } else {
+ self.saved = tbyte;
+ self.run = 1;
+ }
+ } else {
+ self.run += 1;
+ }
+ self.val &= 0xFFFFFF;
+ self.bits -= 8;
+ }
+ }
+ }
+ Ok(())
+ }
+ pub fn flush(mut self) -> EncoderResult<()> {
+ self.flush_run(false)?;
+ self.val <<= 24 - self.bits;
+ self.bw.write_u32be(self.val)?;
+ Ok(())
+ }
+
+ pub fn put_bits(&mut self, val: u32, len: u8) -> EncoderResult<()> {
+ let mut mask = 1 << (len - 1);
+ while mask != 0 {
+ self.put_bool((val & mask) != 0, 128)?;
+ mask >>= 1;
+ }
+ Ok(())
+ }
+ fn put_probability(&mut self, prob: u8) -> EncoderResult<()> {
+ self.put_bits(u32::from(prob >> 1), 7)
+ }
+ fn encode_probability(&mut self, new: u8, old: u8, prob: u8) -> EncoderResult<()> {
+ self.put_bool(new != old, prob)?;
+ if new != old {
+ self.put_probability(new)?;
+ }
+ Ok(())
+ }
+ pub fn write_el<T: PartialEq>(&mut self, el: T, tree: &[TokenSeq<T>], probs: &[u8]) -> EncoderResult<()> {
+ for entry in tree.iter() {
+ if entry.val == el {
+ for seq in entry.seq.iter() {
+ self.put_bool(seq.bit, probs[seq.idx as usize])?;
+ }
+ return Ok(());
+ }
+ }
+ Err(EncoderError::Bug)
+ }
+ fn write_cat(&mut self, cat: i8, tree: &[TokenSeq<i8>], tok_probs: &[u8], val_probs: &[u8; 11]) -> EncoderResult<()> {
+ for entry in tree.iter() {
+ if entry.val == cat {
+ for seq in entry.seq.iter() {
+ let prob = if seq.idx < 5 {
+ tok_probs[seq.idx as usize]
+ } else {
+ val_probs[seq.idx as usize]
+ };
+ self.put_bool(seq.bit, prob)?;
+ }
+ return Ok(());
+ }
+ }
+ Err(EncoderError::Bug)
+ }
+ fn write_large_coef(&mut self, val: i16, cat: usize) -> EncoderResult<()> {
+ let base = VP56_COEF_BASE[cat];
+ let mut probs = VP56_COEF_ADD_PROBS[cat].iter();
+ let add = val.abs() - base;
+ let mut mask = 1 << (VP6_COEF_ADD_BITS[cat] - 1);
+ while mask != 0 {
+ self.put_bool((add & mask) != 0, *probs.next().unwrap())?;
+ mask >>= 1;
+ }
+ self.put_bool(val < 0, 128)?;
+
+ Ok(())
+ }
+ fn write_dc(&mut self, val: i16, tok_probs: &[u8; 5], val_probs: &[u8; 11]) -> EncoderResult<()> {
+ let cat = coef_to_cat(val);
+ self.write_cat(cat, DC_TREE, tok_probs, val_probs)?;
+ if cat < 0 {
+ self.write_large_coef(val, (-cat - 1) as usize)?;
+ } else if val != 0 {
+ self.put_bool(val < 0, 128)?;
+ }
+ Ok(())
+ }
+ fn write_ac(&mut self, val: i16, tree: &[TokenSeq<i8>], probs: &[u8; 11]) -> EncoderResult<()> {
+ let cat = coef_to_cat(val);
+ self.write_cat(cat, tree, probs, probs)?;
+ if cat < 0 {
+ self.write_large_coef(val, (-cat - 1) as usize)?;
+ } else if val != 0 {
+ self.put_bool(val < 0, 128)?;
+ }
+ Ok(())
+ }
+ fn write_zero_run(&mut self, val: usize, probs: &[u8; 14]) -> EncoderResult<()> {
+ self.write_el(val.min(9) as u8, ZERO_RUN_TREE, probs)?;
+ if val >= 9 {
+ let add = val - 9;
+ for i in 0..6 {
+ self.put_bool(((add >> i) & 1) != 0, probs[i + 8])?;
+ }
+ }
+ Ok(())
+ }
+}
+
+fn rescale_mb_mode_prob(prob: u32, total: u32) -> u8 {
+ (255 * prob / (1 + total)) as u8
+}
+
+fn calc_mb_model_probs(prob_xmitted: &[u8; 20], mbtype_models: &mut [VP56MBTypeModel; 10]) {
+ for mode in 0..10 {
+ let mdl = &mut mbtype_models[mode];
+ let mut cnt = [0u32; 10];
+ let mut total = 0;
+ for i in 0..10 {
+ if i == mode { continue; }
+ cnt[i] = 100 * u32::from(prob_xmitted[i * 2]);
+ total += cnt[i];
+ }
+ let sum = u32::from(prob_xmitted[mode * 2]) + u32::from(prob_xmitted[mode * 2 + 1]);
+ mdl.probs[9] = 255 - rescale_mb_mode_prob(u32::from(prob_xmitted[mode * 2 + 1]), sum);
+
+ let inter_mv0_weight = (cnt[0] as u32) + (cnt[2] as u32);
+ let inter_mv1_weight = (cnt[3] as u32) + (cnt[4] as u32);
+ let gold_mv0_weight = (cnt[5] as u32) + (cnt[6] as u32);
+ let gold_mv1_weight = (cnt[8] as u32) + (cnt[9] as u32);
+ let mix_weight = (cnt[1] as u32) + (cnt[7] as u32);
+ mdl.probs[0] = 1 + rescale_mb_mode_prob(inter_mv0_weight + inter_mv1_weight, total);
+ mdl.probs[1] = 1 + rescale_mb_mode_prob(inter_mv0_weight, inter_mv0_weight + inter_mv1_weight);
+ mdl.probs[2] = 1 + rescale_mb_mode_prob(mix_weight, mix_weight + gold_mv0_weight + gold_mv1_weight);
+ mdl.probs[3] = 1 + rescale_mb_mode_prob(cnt[0] as u32, inter_mv0_weight);
+ mdl.probs[4] = 1 + rescale_mb_mode_prob(cnt[3] as u32, inter_mv1_weight);
+ mdl.probs[5] = 1 + rescale_mb_mode_prob(cnt[1], mix_weight);
+ mdl.probs[6] = 1 + rescale_mb_mode_prob(gold_mv0_weight, gold_mv0_weight + gold_mv1_weight);
+ mdl.probs[7] = 1 + rescale_mb_mode_prob(cnt[5], gold_mv0_weight);
+ mdl.probs[8] = 1 + rescale_mb_mode_prob(cnt[8], gold_mv1_weight);
+ }
+}
+
+fn calc_mbtype_bits(prob_xmitted: &[u8; 20], stats: &[[usize; 10]; 10], mdl: &mut [VP56MBTypeModel; 10]) -> u32 {
+ const MB_TYPES: [VPMBType; 10] = [
+ VPMBType::InterNoMV,
+ VPMBType::Intra,
+ VPMBType::InterMV,
+ VPMBType::InterNearest,
+ VPMBType::InterNear,
+ VPMBType::GoldenNoMV,
+ VPMBType::GoldenMV,
+ VPMBType::InterFourMV,
+ VPMBType::GoldenNearest,
+ VPMBType::GoldenNear
+ ];
+
+ calc_mb_model_probs(prob_xmitted, mdl);
+ let mut nits = 0;
+ for (last, (srow, mdl)) in stats.iter().zip(mdl.iter()).enumerate() {
+ for (cur, &ccount) in srow.iter().enumerate() {
+ let ccount = ccount as u32;
+ nits += Estimator::est_nits(cur == last, mdl.probs[9]) * ccount;
+ if cur != last {
+ for entry in MODE_TREE.iter() {
+ if entry.val == MB_TYPES[cur] {
+ for seq in entry.seq.iter() {
+ nits += Estimator::est_nits(seq.bit, mdl.probs[seq.idx as usize]) * ccount;
+ }
+ break;
+ }
+ }
+ }
+ }
+ }
+
+ Estimator::nits_to_bits(nits)
+}
+
+fn find_model_vq(prob_xmitted: &[u8; 20], vq: &[[u8; 20]; 16]) -> usize {
+ let mut best_idx = 0;
+ let mut best_dist = i16::MAX;
+
+ for (idx, row) in vq.iter().enumerate() {
+ let mut dist = 0;
+ for i in 0..20 {
+ let a = prob_xmitted[i ^ 1];
+ let b = row[i];
+ dist += (i16::from(a) - i16::from(b)).abs();
+ }
+ if dist == 0 {
+ return idx;
+ }
+ if dist < best_dist {
+ best_dist = dist;
+ best_idx = idx;
+ }
+ }
+
+ best_idx
+}
+
+// todo per-delta decision, incremental updates and such
+fn deltas_bits(probs: &[u8; 20], base: &[u8; 20], stats: &[[usize; 10]; 10], tmp: &mut [VP56MBTypeModel; 10], deltas: &mut [i16; 20]) -> u32 {
+ const DELTA_PROBS: [u8; 8] = [
+ PROB_BITS[205],
+ PROB_BITS[256 - 205] + PROB_BITS[171] + PROB_BITS[256 - 83] + PROB_BITS[128],
+ PROB_BITS[256 - 205] + PROB_BITS[171] + PROB_BITS[83] + PROB_BITS[128],
+ PROB_BITS[256 - 205] + PROB_BITS[256 - 171] + PROB_BITS[199] + PROB_BITS[256 - 140] + PROB_BITS[128],
+ PROB_BITS[256 - 205] + PROB_BITS[256 - 171] + PROB_BITS[199] + PROB_BITS[140] + PROB_BITS[256 - 125] + PROB_BITS[128],
+ PROB_BITS[256 - 205] + PROB_BITS[256 - 171] + PROB_BITS[199] + PROB_BITS[140] + PROB_BITS[125] + PROB_BITS[256 - 104] + PROB_BITS[128],
+ PROB_BITS[256 - 205] + PROB_BITS[256 - 171] + PROB_BITS[199] + PROB_BITS[140] + PROB_BITS[125] + PROB_BITS[104] + PROB_BITS[128],
+ PROB_BITS[256 - 205] + PROB_BITS[256 - 171] + PROB_BITS[256 - 199] + 8 * PROB_BITS[128],
+ ];
+
+ let mut nits = 0;
+ let mut tprobs = [0u8; 20];
+
+ for i in 0..20 {
+ let old = i16::from(base[i]);
+ let new = i16::from(probs[i]);
+ let mut diff = (new - old) & !3;
+ if old + diff > 255 {
+ diff -= 4;
+ } else if old + diff < 0 || (old + diff == 0 && new != 0) {
+ diff += 4;
+ }
+ tprobs[i] = (old + diff) as u8;
+ deltas[i] = diff;
+ nits += u32::from(DELTA_PROBS[(diff.abs() >> 2).min(7) as usize]);
+ }
+
+ Estimator::nits_to_bits(nits) + calc_mbtype_bits(&tprobs, stats, tmp) + 5
+}
+
+pub fn encode_mode_prob_models(bc: &mut BoolEncoder, models: &mut VP56Models, pmodels: &VP56Models, stats: &[[[usize; 10]; 10]; 3]) -> EncoderResult<()> {
+ let mut tmp = [VP56MBTypeModel::default(); 10];
+ let mut tprob = [0; 20];
+ for ctx in 0..3 {
+ let mut models_changed = models.prob_xmitted[ctx] != pmodels.prob_xmitted[ctx];
+ if models_changed {
+ let old_bits = calc_mbtype_bits(&pmodels.prob_xmitted[ctx], &stats[ctx], &mut tmp);
+ let new_bits = calc_mbtype_bits(&models.prob_xmitted[ctx], &stats[ctx], &mut tmp) + 4;
+ if new_bits < old_bits {
+ let idx = find_model_vq(&models.prob_xmitted[ctx], &VP56_MODE_VQ[ctx]);
+ for i in 0..20 {
+ tprob[i ^ 1] = VP56_MODE_VQ[ctx][idx][i];
+ }
+ let vq_bits = calc_mbtype_bits(&tprob, &stats[ctx], &mut tmp) + 4;
+ if vq_bits < old_bits {
+ bc.put_bool(true, 174)?;
+ bc.put_bits(idx as u32, 4)?;
+ let mut diffs_present = tprob != models.prob_xmitted[ctx];
+ let mut deltas = [0; 20];
+ let delta_cost = deltas_bits(&models.prob_xmitted[ctx], &tprob, &stats[ctx], &mut tmp, &mut deltas);
+ if delta_cost + 1 >= new_bits {
+ diffs_present = false;
+ }
+ if diffs_present {
+ bc.put_bool(true, 254)?;
+ for i in 0..20 {
+ let diff = deltas[i ^ 1] >> 2;
+ bc.put_bool(diff != 0, 205)?;
+ if diff != 0 {
+ let d0 = diff.abs().min(7) as u8;
+ bc.put_bool(diff < 0, 128)?;
+ bc.write_el(d0, MODE_TREE_DIFF, MODE_TREE_DIFF_PROBS)?;
+ if d0 == 7 {
+ bc.put_bits(diff.abs() as u32, 7)?;
+ }
+ tprob[i ^ 1] = (i16::from(tprob[i ^ 1]) + deltas[i ^ 1]) as u8;
+ }
+ }
+ }
+ if !diffs_present {
+ bc.put_bool(false, 254)?;
+ }
+ } else {
+ models_changed = false;
+ }
+ } else {
+ models_changed = false;
+ }
+ }
+ if !models_changed {
+ bc.put_bool(false, 174)?;
+ bc.put_bool(false, 254)?;
+ models.prob_xmitted[ctx] = pmodels.prob_xmitted[ctx];
+ } else {
+ models.prob_xmitted[ctx] = tprob;
+ }
+ }
+ for ctx in 0..3 {
+ let prob_xmitted = &models.prob_xmitted[ctx];
+ calc_mb_model_probs(prob_xmitted, &mut models.mbtype_models[ctx]);
+ }
+ Ok(())
+}
+
+pub fn encode_mv_models(bc: &mut BoolEncoder, models: &[VP56MVModel; 2], pmodels: &[VP56MVModel; 2]) -> EncoderResult<()> {
+ for (i, (mdl, pmdl)) in models.iter().zip(pmodels.iter()).enumerate() {
+ bc.encode_probability(mdl.nz_prob, pmdl.nz_prob, HAS_NZ_PROB[i])?;
+ bc.encode_probability(mdl.sign_prob, pmdl.sign_prob, HAS_SIGN_PROB[i])?;
+ }
+ for (i, (mdl, pmdl)) in models.iter().zip(pmodels.iter()).enumerate() {
+ for (&coded_prob, (&prob, &pprob)) in HAS_TREE_PROB[i].iter().zip(mdl.tree_probs.iter().zip(pmdl.tree_probs.iter())) {
+ bc.encode_probability(prob, pprob, coded_prob)?;
+ }
+ }
+ for (i, (mdl, pmdl)) in models.iter().zip(pmodels.iter()).enumerate() {
+ for (&coded_prob, (&prob, &pprob)) in HAS_RAW_PROB[i].iter().zip(mdl.raw_probs.iter().zip(pmdl.raw_probs.iter())) {
+ bc.encode_probability(prob, pprob, coded_prob)?;
+ }
+ }
+ Ok(())
+}
+
+pub fn encode_coeff_models(bc: &mut BoolEncoder, models: &mut VP56Models, pmodels: &VP56Models, is_intra: bool, interlaced: bool) -> EncoderResult<()> {
+ let mut def_prob = [128u8; 11];
+ for plane in 0..2 {
+ for i in 0..11 {
+ let pprob = pmodels.coeff_models[plane].dc_value_probs[i];
+ let prob = models.coeff_models[plane].dc_value_probs[i];
+ let changed = (is_intra && prob != def_prob[i]) || (!is_intra && prob != pprob);
+ bc.put_bool(changed, HAS_COEF_PROBS[plane][i])?;
+ if changed {
+ bc.put_probability(prob)?;
+ def_prob[i] = prob;
+ }
+ }
+ }
+
+ bc.put_bool(false, 128)?;
+ reset_scan(&mut models.vp6models, interlaced);
+ /* for scan
+ for i in 1..64 {
+ if bc.read_prob(HAS_SCAN_UPD_PROBS[i]) {
+ models.vp6models.scan_order[i] = bc.read_bits(4) as usize;
+ }
+ }
+ update_scan(&mut models.vp6models);
+ */
+
+ for comp in 0..2 {
+ for i in 0..14 {
+ bc.encode_probability(models.vp6models.zero_run_probs[comp][i], pmodels.vp6models.zero_run_probs[comp][i], HAS_ZERO_RUN_PROBS[comp][i])?;
+ }
+ }
+
+ for ctype in 0..3 {
+ for plane in 0..2 {
+ for group in 0..6 {
+ for i in 0..11 {
+ let pprob = pmodels.coeff_models[plane].ac_val_probs[ctype][group][i];
+ let prob = models.coeff_models[plane].ac_val_probs[ctype][group][i];
+ let changed = (is_intra && prob != def_prob[i]) || (!is_intra && prob != pprob);
+ bc.put_bool(changed, VP6_AC_PROBS[ctype][plane][group][i])?;
+ if changed {
+ bc.put_probability(prob)?;
+ def_prob[i] = prob;
+ }
+ }
+ }
+ }
+ }
+
+ for plane in 0..2 {
+ let mdl = &mut models.coeff_models[plane];
+ for i in 0..3 {
+ for k in 0..5 {
+ mdl.dc_token_probs[0][i][k] = rescale_prob(mdl.dc_value_probs[k], &VP6_DC_WEIGHTS[k][i], 255);
+ }
+ }
+ }
+ Ok(())
+}
+
+pub fn encode_block(bc: &mut BoolEncoder, blk: &[i16; 64], dc_mode: usize, model: &VP56CoeffModel, vp6model: &VP6Models) -> EncoderResult<()> {
+ let mut last = 64;
+ for i in (0..64).rev() {
+ if blk[vp6model.zigzag[i]] != 0 {
+ last = i;
+ break;
+ }
+ }
+ if last < 64 {
+ bc.write_dc(blk[0], &model.dc_token_probs[0][dc_mode], &model.dc_value_probs)?;
+ let mut idx = 1;
+ let mut last_idx = 0;
+ let mut last_val = blk[0];
+ while idx <= last {
+ let val = blk[vp6model.zigzag[idx]];
+ let has_nnz = (idx == 1) || (last_val != 0);
+ if (val != 0) || has_nnz {
+ if last_val == 0 && idx != 1 {
+ let zrun = idx - last_idx;
+ bc.write_zero_run(zrun, &vp6model.zero_run_probs[if last_idx + 1 >= 7 { 1 } else { 0 }])?;
+ }
+ let ac_band = VP6_IDX_TO_AC_BAND[idx];
+ let ac_mode = last_val.abs().min(2) as usize;
+ let tree = if has_nnz { COEF_TREE } else { NZ_COEF_TREE };
+ bc.write_ac(val, tree, &model.ac_val_probs[ac_mode][ac_band])?;
+ last_val = val;
+ last_idx = idx;
+ }
+ idx += 1;
+ }
+ if idx < 64 {
+ let ac_band = VP6_IDX_TO_AC_BAND[idx];
+ let ac_mode = last_val.abs().min(2) as usize;
+ bc.write_el(EOB, COEF_TREE, &model.ac_val_probs[ac_mode][ac_band])?;
+ }
+ } else {
+ bc.write_cat(0, DC_TREE, &model.dc_token_probs[0][dc_mode], &model.dc_value_probs)?;
+ let ac_band = VP6_IDX_TO_AC_BAND[1];
+ bc.write_el(EOB, COEF_TREE, &model.ac_val_probs[0][ac_band])?;
+ }
+ Ok(())
+}
+
+fn map_mb_type(mbtype: VPMBType) -> usize {
+ match mbtype {
+ VPMBType::InterNoMV => 0,
+ VPMBType::Intra => 1,
+ VPMBType::InterMV => 2,
+ VPMBType::InterNearest => 3,
+ VPMBType::InterNear => 4,
+ VPMBType::GoldenNoMV => 5,
+ VPMBType::GoldenMV => 6,
+ VPMBType::InterFourMV => 7,
+ VPMBType::GoldenNearest => 8,
+ VPMBType::GoldenNear => 9,
+ }
+}
+
+pub fn encode_mb_type(bc: &mut BoolEncoder, mb_type: VPMBType, last_mb_type: VPMBType, ctx: usize, model: &VP56Models) -> EncoderResult<()> {
+ let probs = &model.mbtype_models[ctx][map_mb_type(last_mb_type)].probs;
+ bc.put_bool(mb_type == last_mb_type, probs[9])?;
+ if mb_type != last_mb_type {
+ bc.write_el(mb_type, MODE_TREE, probs)?;
+ }
+ Ok(())
+}
+
+fn encode_mv_component(bc: &mut BoolEncoder, mv: i16, model: &VP56MVModel) -> EncoderResult<()> {
+ let aval = mv.abs();
+ bc.put_bool(aval >= 8, model.nz_prob)?;
+ if aval < 8 {
+ bc.write_el(aval as u8, SHORT_MV_TREE, &model.tree_probs)?;
+ } else {
+ for &ord in LONG_VECTOR_ORDER.iter() {
+ bc.put_bool(((aval >> ord) & 1) != 0, model.raw_probs[ord])?;
+ }
+ if (aval & 0xF0) != 0 {
+ bc.put_bool((aval & (1 << 3)) != 0, model.raw_probs[3])?;
+ }
+ }
+ if aval != 0 {
+ bc.put_bool(mv < 0, model.sign_prob)?;
+ }
+ Ok(())
+}
+
+pub fn encode_mv(bc: &mut BoolEncoder, mv: MV, model: &VP56Models) -> EncoderResult<()> {
+ encode_mv_component(bc, mv.x, &model.mv_models[0])?;
+ encode_mv_component(bc, mv.y, &model.mv_models[1])?;
+ Ok(())
+}
+
+struct Estimator {}
+
+impl Estimator {
+ fn new() -> Self { Self{} }
+ fn write_el<T: PartialEq>(&self, el: T, tree: &[TokenSeq<T>], probs: &mut [ProbCounter]) {
+ for entry in tree.iter() {
+ if entry.val == el {
+ for seq in entry.seq.iter() {
+ probs[seq.idx as usize].add(seq.bit);
+ }
+ return;
+ }
+ }
+ }
+ fn write_cat(&self, cat: i8, tree: &[TokenSeq<i8>], probs: &mut [ProbCounter; 11]) {
+ for entry in tree.iter() {
+ if entry.val == cat {
+ for seq in entry.seq.iter() {
+ probs[seq.idx as usize].add(seq.bit);
+ }
+ return;
+ }
+ }
+ }
+ fn write_dc(&self, val: i16, probs: &mut [ProbCounter; 11]) {
+ self.write_cat(coef_to_cat(val), DC_TREE, probs);
+ }
+ fn write_ac(&self, val: i16, tree: &[TokenSeq<i8>], probs: &mut [ProbCounter; 11]) {
+ self.write_cat(coef_to_cat(val), tree, probs);
+ }
+ fn write_zero_run(&self, val: usize, probs: &mut [ProbCounter; 14]) {
+ self.write_el(val.min(9) as u8, ZERO_RUN_TREE, probs);
+ if val >= 9 {
+ let add = val - 9;
+ for i in 0..6 {
+ probs[i + 8].add(((add >> i) & 1) != 0);
+ }
+ }
+ }
+ fn est_nits(bit: bool, prob: u8) -> u32 {
+ if !bit {
+ u32::from(PROB_BITS[prob as usize])
+ } else {
+ u32::from(PROB_BITS[256 - (prob as usize)])
+ }
+ }
+ fn nits_to_bits(nits: u32) -> u32 { (nits + 7) >> 3 }
+}
+
+pub fn estimate_block(blk: &[i16; 64], _dc_mode: usize, model: &mut VP56CoeffModelStat, vp6model: &mut VP6ModelsStat, scan: &[usize; 64]) {
+ let bc = Estimator::new();
+
+ let mut last = 64;
+ for i in (0..64).rev() {
+ if blk[scan[i]] != 0 {
+ last = i;
+ break;
+ }
+ }
+ if last < 64 {
+ bc.write_dc(blk[0], &mut model.dc_value_probs);
+ let mut idx = 1;
+ let mut last_idx = 0;
+ let mut last_val = blk[0];
+ while idx <= last {
+ let val = blk[scan[idx]];
+ let has_nnz = (idx == 1) || (last_val != 0);
+ if (val != 0) || has_nnz {
+ if last_val == 0 && idx != 1 {
+ let zrun = idx - last_idx;
+ bc.write_zero_run(zrun, &mut vp6model.zero_run_probs[if last_idx + 1 >= 7 { 1 } else { 0 }]);
+ }
+ let ac_band = VP6_IDX_TO_AC_BAND[idx];
+ let ac_mode = last_val.abs().min(2) as usize;
+ let tree = if has_nnz { COEF_TREE } else { NZ_COEF_TREE };
+ bc.write_ac(val, tree, &mut model.ac_val_probs[ac_mode][ac_band]);
+ last_val = val;
+ last_idx = idx;
+ }
+ idx += 1;
+ }
+ if idx < 64 {
+ let ac_band = VP6_IDX_TO_AC_BAND[idx];
+ let ac_mode = last_val.abs().min(2) as usize;
+ bc.write_el(EOB, COEF_TREE, &mut model.ac_val_probs[ac_mode][ac_band]);
+ }
+ } else {
+ bc.write_cat(0, DC_TREE, &mut model.dc_value_probs);
+ let ac_band = VP6_IDX_TO_AC_BAND[1];
+ bc.write_el(EOB, COEF_TREE, &mut model.ac_val_probs[0][ac_band]);
+ }
+}
+
+pub fn estimate_mb_type(mb_type: VPMBType, last_mb_type: VPMBType, ctx: usize, model: &mut VP56ModelsStat) {
+ model.mbtype_models[ctx][map_mb_type(last_mb_type)][map_mb_type(mb_type)] += 1;
+}
+
+fn estimate_mv_component(mv: i16, model: &mut VP56MVModelStat) {
+ let bc = Estimator::new();
+ let aval = mv.abs();
+ model.nz_prob.add(aval >= 8);
+ if aval < 8 {
+ bc.write_el(aval as u8, SHORT_MV_TREE, &mut model.tree_probs);
+ } else {
+ for &ord in LONG_VECTOR_ORDER.iter() {
+ model.raw_probs[ord].add(((aval >> ord) & 1) != 0);
+ }
+ if (aval & 0xF0) != 0 {
+ model.raw_probs[3].add((aval & (1 << 3)) != 0);
+ }
+ }
+ if aval != 0 {
+ model.sign_prob.add(mv < 0);
+ }
+}
+
+pub fn estimate_mv(mv: MV, model: &mut VP56ModelsStat) {
+ estimate_mv_component(mv.x, &mut model.mv_models[0]);
+ estimate_mv_component(mv.y, &mut model.mv_models[1]);
+}
+
+const VP56_MODE_VQ: [[[u8; 20]; 16]; 3] = [
+ [
+ [ 9, 15, 32, 25, 7, 19, 9, 21, 1, 12, 14, 12, 3, 18, 14, 23, 3, 10, 0, 4 ],
+ [ 48, 39, 1, 2, 11, 27, 29, 44, 7, 27, 1, 4, 0, 3, 1, 6, 1, 2, 0, 0 ],
+ [ 21, 32, 1, 2, 4, 10, 32, 43, 6, 23, 2, 3, 1, 19, 1, 6, 12, 21, 0, 7 ],
+ [ 69, 83, 0, 0, 0, 2, 10, 29, 3, 12, 0, 1, 0, 3, 0, 3, 2, 2, 0, 0 ],
+ [ 11, 20, 1, 4, 18, 36, 43, 48, 13, 35, 0, 2, 0, 5, 3, 12, 1, 2, 0, 0 ],
+ [ 70, 44, 0, 1, 2, 10, 37, 46, 8, 26, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0 ],
+ [ 8, 15, 0, 1, 8, 21, 74, 53, 22, 42, 0, 1, 0, 2, 0, 3, 1, 2, 0, 0 ],
+ [ 141, 42, 0, 0, 1, 4, 11, 24, 1, 11, 0, 1, 0, 1, 0, 2, 0, 0, 0, 0 ],
+ [ 8, 19, 4, 10, 24, 45, 21, 37, 9, 29, 0, 3, 1, 7, 11, 25, 0, 2, 0, 1 ],
+ [ 46, 42, 0, 1, 2, 10, 54, 51, 10, 30, 0, 2, 0, 2, 0, 1, 0, 1, 0, 0 ],
+ [ 28, 32, 0, 0, 3, 10, 75, 51, 14, 33, 0, 1, 0, 2, 0, 1, 1, 2, 0, 0 ],
+ [ 100, 46, 0, 1, 3, 9, 21, 37, 5, 20, 0, 1, 0, 2, 1, 2, 0, 1, 0, 0 ],
+ [ 27, 29, 0, 1, 9, 25, 53, 51, 12, 34, 0, 1, 0, 3, 1, 5, 0, 2, 0, 0 ],
+ [ 80, 38, 0, 0, 1, 4, 69, 33, 5, 16, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0 ],
+ [ 16, 20, 0, 0, 2, 8, 104, 49, 15, 33, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0 ],
+ [ 194, 16, 0, 0, 1, 1, 1, 9, 1, 3, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0 ],
+ ], [
+ [ 41, 22, 1, 0, 1, 31, 0, 0, 0, 0, 0, 1, 1, 7, 0, 1, 98, 25, 4, 10 ],
+ [ 123, 37, 6, 4, 1, 27, 0, 0, 0, 0, 5, 8, 1, 7, 0, 1, 12, 10, 0, 2 ],
+ [ 26, 14, 14, 12, 0, 24, 0, 0, 0, 0, 55, 17, 1, 9, 0, 36, 5, 7, 1, 3 ],
+ [ 209, 5, 0, 0, 0, 27, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0 ],
+ [ 2, 5, 4, 5, 0, 121, 0, 0, 0, 0, 0, 3, 2, 4, 1, 4, 2, 2, 0, 1 ],
+ [ 175, 5, 0, 1, 0, 48, 0, 0, 0, 0, 0, 2, 0, 1, 0, 2, 0, 1, 0, 0 ],
+ [ 83, 5, 2, 3, 0, 102, 0, 0, 0, 0, 1, 3, 0, 2, 0, 1, 0, 0, 0, 0 ],
+ [ 233, 6, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0 ],
+ [ 34, 16, 112, 21, 1, 28, 0, 0, 0, 0, 6, 8, 1, 7, 0, 3, 2, 5, 0, 2 ],
+ [ 159, 35, 2, 2, 0, 25, 0, 0, 0, 0, 3, 6, 0, 5, 0, 1, 4, 4, 0, 1 ],
+ [ 75, 39, 5, 7, 2, 48, 0, 0, 0, 0, 3, 11, 2, 16, 1, 4, 7, 10, 0, 2 ],
+ [ 212, 21, 0, 1, 0, 9, 0, 0, 0, 0, 1, 2, 0, 2, 0, 0, 2, 2, 0, 0 ],
+ [ 4, 2, 0, 0, 0, 172, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 2, 0, 0, 0 ],
+ [ 187, 22, 1, 1, 0, 17, 0, 0, 0, 0, 3, 6, 0, 4, 0, 1, 4, 4, 0, 1 ],
+ [ 133, 6, 1, 2, 1, 70, 0, 0, 0, 0, 0, 2, 0, 4, 0, 3, 1, 1, 0, 0 ],
+ [ 251, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
+ ], [
+ [ 2, 3, 2, 3, 0, 2, 0, 2, 0, 0, 11, 4, 1, 4, 0, 2, 3, 2, 0, 4 ],
+ [ 49, 46, 3, 4, 7, 31, 42, 41, 0, 0, 2, 6, 1, 7, 1, 4, 2, 4, 0, 1 ],
+ [ 26, 25, 1, 1, 2, 10, 67, 39, 0, 0, 1, 1, 0, 14, 0, 2, 31, 26, 1, 6 ],
+ [ 103, 46, 1, 2, 2, 10, 33, 42, 0, 0, 1, 4, 0, 3, 0, 1, 1, 3, 0, 0 ],
+ [ 14, 31, 9, 13, 14, 54, 22, 29, 0, 0, 2, 6, 4, 18, 6, 13, 1, 5, 0, 1 ],
+ [ 85, 39, 0, 0, 1, 9, 69, 40, 0, 0, 0, 1, 0, 3, 0, 1, 2, 3, 0, 0 ],
+ [ 31, 28, 0, 0, 3, 14, 130, 34, 0, 0, 0, 1, 0, 3, 0, 1, 3, 3, 0, 1 ],
+ [ 171, 25, 0, 0, 1, 5, 25, 21, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0 ],
+ [ 17, 21, 68, 29, 6, 15, 13, 22, 0, 0, 6, 12, 3, 14, 4, 10, 1, 7, 0, 3 ],
+ [ 51, 39, 0, 1, 2, 12, 91, 44, 0, 0, 0, 2, 0, 3, 0, 1, 2, 3, 0, 1 ],
+ [ 81, 25, 0, 0, 2, 9, 106, 26, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0 ],
+ [ 140, 37, 0, 1, 1, 8, 24, 33, 0, 0, 1, 2, 0, 2, 0, 1, 1, 2, 0, 0 ],
+ [ 14, 23, 1, 3, 11, 53, 90, 31, 0, 0, 0, 3, 1, 5, 2, 6, 1, 2, 0, 0 ],
+ [ 123, 29, 0, 0, 1, 7, 57, 30, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0 ],
+ [ 13, 14, 0, 0, 4, 20, 175, 20, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0 ],
+ [ 202, 23, 0, 0, 1, 3, 2, 9, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0 ],
+ ]
+];
projects / nihav.git / blobdiff