238 lines
7.1 KiB
C
238 lines
7.1 KiB
C
#include <stdio.h>
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#include <stdlib.h>
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#include <math.h>
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#include "vadpcm.h"
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void vencodeframe(FILE *ofile, s16 *inBuffer, s32 *state, s32 ***coefTable, s32 order, s32 npredictors, s32 nsam)
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{
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s16 ix[16];
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s32 prediction[16];
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s32 inVector[16];
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s32 saveState[16];
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s32 optimalp;
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s32 scale;
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s32 llevel;
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s32 ulevel;
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s32 i;
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s32 j;
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s32 k;
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s32 ie[16];
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s32 nIter;
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s32 max;
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s32 cV;
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s32 maxClip;
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u8 header;
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u8 c;
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f32 e[16];
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f32 se;
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f32 min;
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// We are only given 'nsam' samples; pad with zeroes to 16.
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for (i = nsam; i < 16; i++)
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{
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inBuffer[i] = 0;
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}
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llevel = -8;
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ulevel = -llevel - 1;
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// Determine the best-fitting predictor.
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min = 1e30;
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optimalp = 0;
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for (k = 0; k < npredictors; k++)
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{
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// Copy over the last 'order' samples from the previous output.
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for (i = 0; i < order; i++)
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{
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inVector[i] = state[16 - order + i];
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}
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// For 8 samples...
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for (i = 0; i < 8; i++)
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{
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// Compute a prediction based on 'order' values from the old state,
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// plus previous errors in this chunk, as an inner product with the
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// coefficient table.
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prediction[i] = inner_product(order + i, coefTable[k][i], inVector);
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// Record the error in inVector (thus, its first 'order' samples
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// will contain actual values, the rest will be error terms), and
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// in floating point form in e (for no particularly good reason).
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inVector[i + order] = inBuffer[i] - prediction[i];
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e[i] = (f32) inVector[i + order];
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}
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// For the next 8 samples, start with 'order' values from the end of
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// the previous 8-sample chunk of inBuffer. (The code is equivalent to
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// inVector[i] = inBuffer[8 - order + i].)
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for (i = 0; i < order; i++)
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{
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inVector[i] = prediction[8 - order + i] + inVector[8 + i];
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}
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// ... and do the same thing as before to get predictions.
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for (i = 0; i < 8; i++)
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{
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prediction[8 + i] = inner_product(order + i, coefTable[k][i], inVector);
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inVector[i + order] = inBuffer[8 + i] - prediction[8 + i];
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e[8 + i] = (f32) inVector[i + order];
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}
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// Compute the L2 norm of the errors; the lowest norm decides which
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// predictor to use.
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se = 0.0f;
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for (j = 0; j < 16; j++)
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{
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se += e[j] * e[j];
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}
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if (se < min)
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{
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min = se;
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optimalp = k;
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}
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}
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// Do exactly the same thing again, for real.
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for (i = 0; i < order; i++)
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{
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inVector[i] = state[16 - order + i];
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}
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for (i = 0; i < 8; i++)
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{
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if (coefTable == NULL || coefTable[optimalp] == NULL) { continue; }
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prediction[i] = inner_product(order + i, coefTable[optimalp][i], inVector);
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inVector[i + order] = inBuffer[i] - prediction[i];
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e[i] = (f32) inVector[i + order];
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}
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for (i = 0; i < order; i++)
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{
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inVector[i] = prediction[8 - order + i] + inVector[8 + i];
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}
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for (i = 0; i < 8; i++)
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{
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prediction[8 + i] = inner_product(order + i, coefTable[optimalp][i], inVector);
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inVector[i + order] = inBuffer[8 + i] - prediction[8 + i];
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e[8 + i] = (f32) inVector[i + order];
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}
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// Clamp the errors to 16-bit signed ints, and put them in ie.
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clamp(16, e, ie, 16);
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// Find a value with highest absolute value.
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// @bug If this first finds -2^n and later 2^n, it should set 'max' to the
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// latter, which needs a higher value for 'scale'.
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max = 0;
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for (i = 0; i < 16; i++)
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{
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if (fabs((f64)ie[i]) > fabs((f64)max))
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{
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max = ie[i];
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}
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}
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// Compute which power of two we need to scale down by in order to make
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// all values representable as 4-bit signed integers (i.e. be in [-8, 7]).
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// The worst-case 'max' is -2^15, so this will be at most 12.
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for (scale = 0; scale <= 12; scale++)
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{
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if (max <= ulevel && max >= llevel)
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{
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goto out;
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}
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max /= 2;
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}
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out:;
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for (i = 0; i < 16; i++)
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{
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saveState[i] = state[i];
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}
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// Try with the computed scale, but if it turns out we don't fit in 4 bits
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// (if some |cV| >= 2), use scale + 1 instead (i.e. downscaling by another
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// factor of 2).
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scale--;
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nIter = 0;
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do
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{
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nIter++;
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maxClip = 0;
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scale++;
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if (scale > 12)
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{
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scale = 12;
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}
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// Copy over the last 'order' samples from the previous output.
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for (i = 0; i < order; i++)
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{
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inVector[i] = saveState[16 - order + i];
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}
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// For 8 samples...
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for (i = 0; i < 8; i++)
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{
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// Compute a prediction based on 'order' values from the old state,
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// plus previous *quantized* errors in this chunk (because that's
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// all the decoder will have available).
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prediction[i] = inner_product(order + i, coefTable[optimalp][i], inVector);
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// Compute the error, and divide it by 2^scale, rounding to the
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// nearest integer. This should ideally result in a 4-bit integer.
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se = (f32) inBuffer[i] - (f32) prediction[i];
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ix[i] = qsample(se, 1 << scale);
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// Clamp the error to a 4-bit signed integer, and record what delta
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// was needed for that.
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cV = (s16) clip(ix[i], llevel, ulevel) - ix[i];
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if (maxClip < abs(cV))
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{
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maxClip = abs(cV);
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}
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ix[i] += cV;
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// Record the quantized error in inVector for later predictions,
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// and the quantized (decoded) output in state (for use in the next
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// batch of 8 samples).
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inVector[i + order] = ix[i] * (1 << scale);
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state[i] = prediction[i] + inVector[i + order];
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}
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// Copy over the last 'order' decoded samples from the above chunk.
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for (i = 0; i < order; i++)
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{
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inVector[i] = state[8 - order + i];
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}
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// ... and do the same thing as before.
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for (i = 0; i < 8; i++)
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{
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prediction[8 + i] = inner_product(order + i, coefTable[optimalp][i], inVector);
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se = (f32) inBuffer[8 + i] - (f32) prediction[8 + i];
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ix[8 + i] = qsample(se, 1 << scale);
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cV = (s16) clip(ix[8 + i], llevel, ulevel) - ix[8 + i];
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if (maxClip < abs(cV))
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{
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maxClip = abs(cV);
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}
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ix[8 + i] += cV;
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inVector[i + order] = ix[8 + i] * (1 << scale);
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state[8 + i] = prediction[8 + i] + inVector[i + order];
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}
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}
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while (maxClip >= 2 && nIter < 2);
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// The scale, the predictor index, and the 16 computed outputs are now all
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// 4-bit numbers. Write them out as 1 + 8 bytes.
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header = (scale << 4) | (optimalp & 0xf);
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fwrite(&header, 1, 1, ofile);
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for (i = 0; i < 16; i += 2)
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{
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c = (ix[i] << 4) | (ix[i + 1] & 0xf);
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fwrite(&c, 1, 1, ofile);
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}
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}
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