/* ** Copyright 2003-2010, VisualOn, Inc. ** ** Licensed under the Apache License, Version 2.0 (the "License"); ** you may not use this file except in compliance with the License. ** You may obtain a copy of the License at ** ** http://www.apache.org/licenses/LICENSE-2.0 ** ** Unless required by applicable law or agreed to in writing, software ** distributed under the License is distributed on an "AS IS" BASIS, ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ** See the License for the specific language governing permissions and ** limitations under the License. */ /*********************************************************************** * File: apisf_2s.c * * * * Description: Coding/Decodeing of ISF parameters with predication * The ISF vector is quantized using two-stage VQ with split-by-2 * * in 1st stage and split-by-5(or 3) in the second stage * * * ************************************************************************/ #include "typedef.h" #include "basic_op.h" #include "cnst.h" #include "acelp.h" #include "qpisf_2s.tab" /* Codebooks of isfs */ #define MU 10923 /* Prediction factor (1.0/3.0) in Q15 */ #define N_SURV_MAX 4 /* 4 survivors max */ #define ALPHA 29491 /* 0. 9 in Q15 */ #define ONE_ALPHA (32768-ALPHA) /* (1.0 - ALPHA) in Q15 */ /* private functions */ static void VQ_stage1( Word16 * x, /* input : ISF residual vector */ Word16 * dico, /* input : quantization codebook */ Word16 dim, /* input : dimention of vector */ Word16 dico_size, /* input : size of quantization codebook */ Word16 * index, /* output: indices of survivors */ Word16 surv /* input : number of survivor */ ); /************************************************************************** * Function: Qpisf_2s_46B() * * * * Description: Quantization of isf parameters with prediction. (46 bits) * * * * The isf vector is quantized using two-stage VQ with split-by-2 in * * 1st stage and split-by-5 in the second stage. * ***************************************************************************/ void Qpisf_2s_46b( Word16 * isf1, /* (i) Q15 : ISF in the frequency domain (0..0.5) */ Word16 * isf_q, /* (o) Q15 : quantized ISF (0..0.5) */ Word16 * past_isfq, /* (io)Q15 : past ISF quantizer */ Word16 * indice, /* (o) : quantization indices */ Word16 nb_surv /* (i) : number of survivor (1, 2, 3 or 4) */ ) { Word16 tmp_ind[5]; Word16 surv1[N_SURV_MAX]; /* indices of survivors from 1st stage */ Word32 i, k, temp, min_err, distance; Word16 isf[ORDER]; Word16 isf_stage2[ORDER]; for (i = 0; i < ORDER; i++) { isf[i] = vo_sub(isf1[i], mean_isf[i]); isf[i] = vo_sub(isf[i], vo_mult(MU, past_isfq[i])); } VQ_stage1(&isf[0], dico1_isf, 9, SIZE_BK1, surv1, nb_surv); distance = MAX_32; for (k = 0; k < nb_surv; k++) { for (i = 0; i < 9; i++) { isf_stage2[i] = vo_sub(isf[i], dico1_isf[i + surv1[k] * 9]); } tmp_ind[0] = Sub_VQ(&isf_stage2[0], dico21_isf, 3, SIZE_BK21, &min_err); temp = min_err; tmp_ind[1] = Sub_VQ(&isf_stage2[3], dico22_isf, 3, SIZE_BK22, &min_err); temp = vo_L_add(temp, min_err); tmp_ind[2] = Sub_VQ(&isf_stage2[6], dico23_isf, 3, SIZE_BK23, &min_err); temp = vo_L_add(temp, min_err); if(temp < distance) { distance = temp; indice[0] = surv1[k]; for (i = 0; i < 3; i++) { indice[i + 2] = tmp_ind[i]; } } } VQ_stage1(&isf[9], dico2_isf, 7, SIZE_BK2, surv1, nb_surv); distance = MAX_32; for (k = 0; k < nb_surv; k++) { for (i = 0; i < 7; i++) { isf_stage2[i] = vo_sub(isf[9 + i], dico2_isf[i + surv1[k] * 7]); } tmp_ind[0] = Sub_VQ(&isf_stage2[0], dico24_isf, 3, SIZE_BK24, &min_err); temp = min_err; tmp_ind[1] = Sub_VQ(&isf_stage2[3], dico25_isf, 4, SIZE_BK25, &min_err); temp = vo_L_add(temp, min_err); if(temp < distance) { distance = temp; indice[1] = surv1[k]; for (i = 0; i < 2; i++) { indice[i + 5] = tmp_ind[i]; } } } Dpisf_2s_46b(indice, isf_q, past_isfq, isf_q, isf_q, 0, 0); return; } /***************************************************************************** * Function: Qpisf_2s_36B() * * * * Description: Quantization of isf parameters with prediction. (36 bits) * * * * The isf vector is quantized using two-stage VQ with split-by-2 in * * 1st stage and split-by-3 in the second stage. * ******************************************************************************/ void Qpisf_2s_36b( Word16 * isf1, /* (i) Q15 : ISF in the frequency domain (0..0.5) */ Word16 * isf_q, /* (o) Q15 : quantized ISF (0..0.5) */ Word16 * past_isfq, /* (io)Q15 : past ISF quantizer */ Word16 * indice, /* (o) : quantization indices */ Word16 nb_surv /* (i) : number of survivor (1, 2, 3 or 4) */ ) { Word16 i, k, tmp_ind[5]; Word16 surv1[N_SURV_MAX]; /* indices of survivors from 1st stage */ Word32 temp, min_err, distance; Word16 isf[ORDER]; Word16 isf_stage2[ORDER]; for (i = 0; i < ORDER; i++) { isf[i] = vo_sub(isf1[i], mean_isf[i]); isf[i] = vo_sub(isf[i], vo_mult(MU, past_isfq[i])); } VQ_stage1(&isf[0], dico1_isf, 9, SIZE_BK1, surv1, nb_surv); distance = MAX_32; for (k = 0; k < nb_surv; k++) { for (i = 0; i < 9; i++) { isf_stage2[i] = vo_sub(isf[i], dico1_isf[i + surv1[k] * 9]); } tmp_ind[0] = Sub_VQ(&isf_stage2[0], dico21_isf_36b, 5, SIZE_BK21_36b, &min_err); temp = min_err; tmp_ind[1] = Sub_VQ(&isf_stage2[5], dico22_isf_36b, 4, SIZE_BK22_36b, &min_err); temp = vo_L_add(temp, min_err); if(temp < distance) { distance = temp; indice[0] = surv1[k]; for (i = 0; i < 2; i++) { indice[i + 2] = tmp_ind[i]; } } } VQ_stage1(&isf[9], dico2_isf, 7, SIZE_BK2, surv1, nb_surv); distance = MAX_32; for (k = 0; k < nb_surv; k++) { for (i = 0; i < 7; i++) { isf_stage2[i] = vo_sub(isf[9 + i], dico2_isf[i + surv1[k] * 7]); } tmp_ind[0] = Sub_VQ(&isf_stage2[0], dico23_isf_36b, 7, SIZE_BK23_36b, &min_err); temp = min_err; if(temp < distance) { distance = temp; indice[1] = surv1[k]; indice[4] = tmp_ind[0]; } } Dpisf_2s_36b(indice, isf_q, past_isfq, isf_q, isf_q, 0, 0); return; } /********************************************************************* * Function: Dpisf_2s_46b() * * * * Description: Decoding of ISF parameters * **********************************************************************/ void Dpisf_2s_46b( Word16 * indice, /* input: quantization indices */ Word16 * isf_q, /* output: quantized ISF in frequency domain (0..0.5) */ Word16 * past_isfq, /* i/0 : past ISF quantizer */ Word16 * isfold, /* input : past quantized ISF */ Word16 * isf_buf, /* input : isf buffer */ Word16 bfi, /* input : Bad frame indicator */ Word16 enc_dec ) { Word16 ref_isf[M], tmp; Word32 i, j, L_tmp; if (bfi == 0) /* Good frame */ { for (i = 0; i < 9; i++) { isf_q[i] = dico1_isf[indice[0] * 9 + i]; } for (i = 0; i < 7; i++) { isf_q[i + 9] = dico2_isf[indice[1] * 7 + i]; } for (i = 0; i < 3; i++) { isf_q[i] = add1(isf_q[i], dico21_isf[indice[2] * 3 + i]); isf_q[i + 3] = add1(isf_q[i + 3], dico22_isf[indice[3] * 3 + i]); isf_q[i + 6] = add1(isf_q[i + 6], dico23_isf[indice[4] * 3 + i]); isf_q[i + 9] = add1(isf_q[i + 9], dico24_isf[indice[5] * 3 + i]); } for (i = 0; i < 4; i++) { isf_q[i + 12] = add1(isf_q[i + 12], dico25_isf[indice[6] * 4 + i]); } for (i = 0; i < ORDER; i++) { tmp = isf_q[i]; isf_q[i] = add1(tmp, mean_isf[i]); isf_q[i] = add1(isf_q[i], vo_mult(MU, past_isfq[i])); past_isfq[i] = tmp; } if (enc_dec) { for (i = 0; i < M; i++) { for (j = (L_MEANBUF - 1); j > 0; j--) { isf_buf[j * M + i] = isf_buf[(j - 1) * M + i]; } isf_buf[i] = isf_q[i]; } } } else { /* bad frame */ for (i = 0; i < M; i++) { L_tmp = mean_isf[i] << 14; for (j = 0; j < L_MEANBUF; j++) { L_tmp += (isf_buf[j * M + i] << 14); } ref_isf[i] = vo_round(L_tmp); } /* use the past ISFs slightly shifted towards their mean */ for (i = 0; i < ORDER; i++) { isf_q[i] = add1(vo_mult(ALPHA, isfold[i]), vo_mult(ONE_ALPHA, ref_isf[i])); } /* estimate past quantized residual to be used in next frame */ for (i = 0; i < ORDER; i++) { tmp = add1(ref_isf[i], vo_mult(past_isfq[i], MU)); /* predicted ISF */ past_isfq[i] = vo_sub(isf_q[i], tmp); past_isfq[i] = (past_isfq[i] >> 1); /* past_isfq[i] *= 0.5 */ } } Reorder_isf(isf_q, ISF_GAP, ORDER); return; } /********************************************************************* * Function: Disf_2s_36b() * * * * Description: Decoding of ISF parameters * *********************************************************************/ void Dpisf_2s_36b( Word16 * indice, /* input: quantization indices */ Word16 * isf_q, /* output: quantized ISF in frequency domain (0..0.5) */ Word16 * past_isfq, /* i/0 : past ISF quantizer */ Word16 * isfold, /* input : past quantized ISF */ Word16 * isf_buf, /* input : isf buffer */ Word16 bfi, /* input : Bad frame indicator */ Word16 enc_dec ) { Word16 ref_isf[M], tmp; Word32 i, j, L_tmp; if (bfi == 0) /* Good frame */ { for (i = 0; i < 9; i++) { isf_q[i] = dico1_isf[indice[0] * 9 + i]; } for (i = 0; i < 7; i++) { isf_q[i + 9] = dico2_isf[indice[1] * 7 + i]; } for (i = 0; i < 5; i++) { isf_q[i] = add1(isf_q[i], dico21_isf_36b[indice[2] * 5 + i]); } for (i = 0; i < 4; i++) { isf_q[i + 5] = add1(isf_q[i + 5], dico22_isf_36b[indice[3] * 4 + i]); } for (i = 0; i < 7; i++) { isf_q[i + 9] = add1(isf_q[i + 9], dico23_isf_36b[indice[4] * 7 + i]); } for (i = 0; i < ORDER; i++) { tmp = isf_q[i]; isf_q[i] = add1(tmp, mean_isf[i]); isf_q[i] = add1(isf_q[i], vo_mult(MU, past_isfq[i])); past_isfq[i] = tmp; } if (enc_dec) { for (i = 0; i < M; i++) { for (j = (L_MEANBUF - 1); j > 0; j--) { isf_buf[j * M + i] = isf_buf[(j - 1) * M + i]; } isf_buf[i] = isf_q[i]; } } } else { /* bad frame */ for (i = 0; i < M; i++) { L_tmp = (mean_isf[i] << 14); for (j = 0; j < L_MEANBUF; j++) { L_tmp += (isf_buf[j * M + i] << 14); } ref_isf[i] = vo_round(L_tmp); } /* use the past ISFs slightly shifted towards their mean */ for (i = 0; i < ORDER; i++) { isf_q[i] = add1(vo_mult(ALPHA, isfold[i]), vo_mult(ONE_ALPHA, ref_isf[i])); } /* estimate past quantized residual to be used in next frame */ for (i = 0; i < ORDER; i++) { tmp = add1(ref_isf[i], vo_mult(past_isfq[i], MU)); /* predicted ISF */ past_isfq[i] = vo_sub(isf_q[i], tmp); past_isfq[i] = past_isfq[i] >> 1; /* past_isfq[i] *= 0.5 */ } } Reorder_isf(isf_q, ISF_GAP, ORDER); return; } /*************************************************************************** * Function: Reorder_isf() * * * * Description: To make sure that the isfs are properly order and to * * keep a certain minimum distance between consecutive isfs. * *--------------------------------------------------------------------------* * Argument description in/out * * * * isf[] vector of isfs i/o * * min_dist minimum required distance i * * n LPC order i * ****************************************************************************/ void Reorder_isf( Word16 * isf, /* (i/o) Q15: ISF in the frequency domain (0..0.5) */ Word16 min_dist, /* (i) Q15 : minimum distance to keep */ Word16 n /* (i) : number of ISF */ ) { Word32 i; Word16 isf_min; isf_min = min_dist; for (i = 0; i < n - 1; i++) { if(isf[i] < isf_min) { isf[i] = isf_min; } isf_min = (isf[i] + min_dist); } return; } Word16 Sub_VQ( /* output: return quantization index */ Word16 * x, /* input : ISF residual vector */ Word16 * dico, /* input : quantization codebook */ Word16 dim, /* input : dimention of vector */ Word16 dico_size, /* input : size of quantization codebook */ Word32 * distance /* output: error of quantization */ ) { Word16 temp, *p_dico; Word32 i, j, index; Word32 dist_min, dist; dist_min = MAX_32; p_dico = dico; index = 0; for (i = 0; i < dico_size; i++) { dist = 0; for (j = 0; j < dim; j++) { temp = x[j] - (*p_dico++); dist += (temp * temp)<<1; } if(dist < dist_min) { dist_min = dist; index = i; } } *distance = dist_min; /* Reading the selected vector */ p_dico = &dico[index * dim]; for (j = 0; j < dim; j++) { x[j] = *p_dico++; } return index; } static void VQ_stage1( Word16 * x, /* input : ISF residual vector */ Word16 * dico, /* input : quantization codebook */ Word16 dim, /* input : dimention of vector */ Word16 dico_size, /* input : size of quantization codebook */ Word16 * index, /* output: indices of survivors */ Word16 surv /* input : number of survivor */ ) { Word16 temp, *p_dico; Word32 i, j, k, l; Word32 dist_min[N_SURV_MAX], dist; dist_min[0] = MAX_32; dist_min[1] = MAX_32; dist_min[2] = MAX_32; dist_min[3] = MAX_32; index[0] = 0; index[1] = 1; index[2] = 2; index[3] = 3; p_dico = dico; for (i = 0; i < dico_size; i++) { dist = 0; for (j = 0; j < dim; j++) { temp = x[j] - (*p_dico++); dist += (temp * temp)<<1; } for (k = 0; k < surv; k++) { if(dist < dist_min[k]) { for (l = surv - 1; l > k; l--) { dist_min[l] = dist_min[l - 1]; index[l] = index[l - 1]; } dist_min[k] = dist; index[k] = i; break; } } } return; }