Joonas Rikkonen
135 lines
6.9 KiB
C#
135 lines
6.9 KiB
C#
/* Copyright (c) 2006-2011 Skype Limited. All Rights Reserved
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Ported to C# by Logan Stromberg
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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- Neither the name of Internet Society, IETF or IETF Trust, nor the
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names of specific contributors, may be used to endorse or promote
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products derived from this software without specific prior written
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permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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namespace Concentus.Silk
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{
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using Concentus.Common;
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using Concentus.Common.CPlusPlus;
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using Concentus.Silk.Enums;
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using Concentus.Silk.Structs;
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using System.Diagnostics;
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internal static class VQ_WMat_EC
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{
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/* Entropy constrained matrix-weighted VQ, hard-coded to 5-element vectors, for a single input data vector */
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internal static void silk_VQ_WMat_EC(
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BoxedValueSbyte ind, /* O index of best codebook vector */
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BoxedValueInt rate_dist_Q14, /* O best weighted quant error + mu * rate */
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BoxedValueInt gain_Q7, /* O sum of absolute LTP coefficients */
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short[] in_Q14, /* I input vector to be quantized */
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int in_Q14_ptr,
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int[] W_Q18, /* I weighting matrix */
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int W_Q18_ptr,
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sbyte[][] cb_Q7, /* I codebook */
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byte[] cb_gain_Q7, /* I codebook effective gain */
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byte[] cl_Q5, /* I code length for each codebook vector */
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int mu_Q9, /* I tradeoff betw. weighted error and rate */
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int max_gain_Q7, /* I maximum sum of absolute LTP coefficients */
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int L /* I number of vectors in codebook */
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)
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{
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int k, gain_tmp_Q7;
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sbyte[] cb_row_Q7;
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int cb_row_Q7_ptr = 0;
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short[] diff_Q14 = new short[5];
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int sum1_Q14, sum2_Q16;
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/* Loop over codebook */
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rate_dist_Q14.Val = int.MaxValue;
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for (k = 0; k < L; k++)
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{
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/* Go to next cbk vector */
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cb_row_Q7 = cb_Q7[cb_row_Q7_ptr++];
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gain_tmp_Q7 = cb_gain_Q7[k];
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diff_Q14[0] = (short)(in_Q14[in_Q14_ptr] - Inlines.silk_LSHIFT(cb_row_Q7[0], 7));
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diff_Q14[1] = (short)(in_Q14[in_Q14_ptr + 1] - Inlines.silk_LSHIFT(cb_row_Q7[1], 7));
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diff_Q14[2] = (short)(in_Q14[in_Q14_ptr + 2] - Inlines.silk_LSHIFT(cb_row_Q7[2], 7));
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diff_Q14[3] = (short)(in_Q14[in_Q14_ptr + 3] - Inlines.silk_LSHIFT(cb_row_Q7[3], 7));
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diff_Q14[4] = (short)(in_Q14[in_Q14_ptr + 4] - Inlines.silk_LSHIFT(cb_row_Q7[4], 7));
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/* Weighted rate */
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sum1_Q14 = Inlines.silk_SMULBB(mu_Q9, cl_Q5[k]);
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/* Penalty for too large gain */
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sum1_Q14 = Inlines.silk_ADD_LSHIFT32(sum1_Q14, Inlines.silk_max(Inlines.silk_SUB32(gain_tmp_Q7, max_gain_Q7), 0), 10);
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Inlines.OpusAssert(sum1_Q14 >= 0);
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/* first row of W_Q18 */
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sum2_Q16 = Inlines.silk_SMULWB(W_Q18[W_Q18_ptr + 1], diff_Q14[1]);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 2], diff_Q14[2]);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 3], diff_Q14[3]);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 4], diff_Q14[4]);
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sum2_Q16 = Inlines.silk_LSHIFT(sum2_Q16, 1);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr], diff_Q14[0]);
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sum1_Q14 = Inlines.silk_SMLAWB(sum1_Q14, sum2_Q16, diff_Q14[0]);
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/* second row of W_Q18 */
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sum2_Q16 = Inlines.silk_SMULWB(W_Q18[W_Q18_ptr + 7], diff_Q14[2]);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 8], diff_Q14[3]);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 9], diff_Q14[4]);
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sum2_Q16 = Inlines.silk_LSHIFT(sum2_Q16, 1);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 6], diff_Q14[1]);
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sum1_Q14 = Inlines.silk_SMLAWB(sum1_Q14, sum2_Q16, diff_Q14[1]);
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/* third row of W_Q18 */
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sum2_Q16 = Inlines.silk_SMULWB(W_Q18[W_Q18_ptr + 13], diff_Q14[3]);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 14], diff_Q14[4]);
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sum2_Q16 = Inlines.silk_LSHIFT(sum2_Q16, 1);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 12], diff_Q14[2]);
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sum1_Q14 = Inlines.silk_SMLAWB(sum1_Q14, sum2_Q16, diff_Q14[2]);
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/* fourth row of W_Q18 */
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sum2_Q16 = Inlines.silk_SMULWB(W_Q18[W_Q18_ptr + 19], diff_Q14[4]);
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sum2_Q16 = Inlines.silk_LSHIFT(sum2_Q16, 1);
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sum2_Q16 = Inlines.silk_SMLAWB(sum2_Q16, W_Q18[W_Q18_ptr + 18], diff_Q14[3]);
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sum1_Q14 = Inlines.silk_SMLAWB(sum1_Q14, sum2_Q16, diff_Q14[3]);
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/* last row of W_Q18 */
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sum2_Q16 = Inlines.silk_SMULWB(W_Q18[W_Q18_ptr + 24], diff_Q14[4]);
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sum1_Q14 = Inlines.silk_SMLAWB(sum1_Q14, sum2_Q16, diff_Q14[4]);
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Inlines.OpusAssert(sum1_Q14 >= 0);
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/* find best */
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if (sum1_Q14 < rate_dist_Q14.Val)
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{
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rate_dist_Q14.Val = sum1_Q14;
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ind.Val = (sbyte)k;
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gain_Q7.Val = gain_tmp_Q7;
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}
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}
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}
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}
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}
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