double intervalMSE(const SignalSource &signal1, const SignalSource &signal2, int step)
{
qDebug() << "create interval MSE";
auto aver1 = averagePowers(signal1, step, false);
auto aver2 = averagePowers(signal2, step, false);
return MSE(aver1, aver2);
}
int main(int argc, char *argv[]){
int * a;
int * b;
double t;
const unsigned int N = (argc >= 2) ? atoi(argv[1]) : 8;
const unsigned int k1 = (argc >= 3) ? atoi(argv[2]) : 7;
const unsigned int k2 = (argc >= 4) ? atoi(argv[3]) : 9;
double resultado;
t = tick();
a = malloc(N*N*sizeof(int));
b = malloc(N*N*sizeof(int));
init_matrix(a, N, 0, k1);
init_matrix(b, N, 1, k2);
//a = read_ppm5("dos00064.pgm",&h, &w, &bbp);
//b = read_ppm5("dos00065.pgm",&h, &w, &bbp);
t = tick() - t;
printf("Alocacion y lectura de las matrices: %f\n", t);
//b[0][0]=-22;
t = tick();
resultado = MSE(a,b,N);
t = tick() - t;
printf("Procesamiento MSE en matrices: %f\n", t);
printf("\x1B[33mResultado final =====>>> %f\x1B[0m\n", resultado);
if (resultado == verificar (N, k1, k2))
printf("\x1B[32mVerificación: %f == %f\x1B[0m\n", resultado, verificar (N, k1, k2));
else
printf("\x1B[31mVerificación: %f == %f\x1B[0m\n", resultado, verificar (N, k1, k2));
return 0;
}
double PSNR(PPMImage *img,PPMImage *img2){
double PSNR_;
double MSE_;
MSE_ = MSE(img,img2);
MSE_ = 255 / sqrt(MSE_);
PSNR_ = 20*log10(MSE_);
return PSNR_;
}
int main() {
freopen("Lena.raw", "rb", stdin);
int r, c;
for (r = 0; r < N; r++)
for (c = 0; c < N; c++)
scanf("%c", &pic[r][c]);
init();
DCT();
quantization();
IDCT();
MSE();
return 0;
}
CAPDU build_CA_Step_B(const OBJECT_IDENTIFIER_t& CA_OID, const unsigned char sessionid)
{
MSE mse = MSE(MSE::P1_SET | MSE::P1_COMPUTE, MSE::P2_AT);
// Build up command data field
std::vector<unsigned char> oid(CA_OID.buf, CA_OID.buf+CA_OID.size);;
std::vector<unsigned char> data = TLV_encode(0x80, oid);
if (sessionid) {
data.push_back(0xE0);
data.push_back(0x03);
data.push_back(0x81);
data.push_back(0x01);
data.push_back(sessionid);
}
mse.setData(data);
return mse;
}
int main(int argc, char* argv[])
{
double x[] = {60, 65, 70, 65, 66, 68, 69, 62, 65, 66, 68, 59, 60, 64,
66, 68, 59, 60, 64, 60, 64, 66, 68, 59, 60, 64, 59, 60, 64, 66, 69, 67};
double y[32];
int N = sizeof(x)/sizeof(double);
int m = 4;
for(int scale=1;scale<20;scale++)
{
int len_y = changeScale(x, y, N, scale);
if(len_y <= m) break;
for(int i=0;i<len_y;i++)
printf("%f ", y[i]);
double mse = MSE(y, len_y, m);
printf("\n--------------------------------------------------------\n");
printf("%f\n\n\n", mse);
}
return 0;
}
/**
* @brief RMSE computes the root mean squared error (RMSE) between two images.
* @param ori is the original image.
* @param cmp is the distorted image.
* @param bLargeDifferences, if true, skips big differences for stability.
* @param type is the domain where to compute RMSE (linear, logarithmic, and PU).
* @return It returns the MSE value between ori and cmp.
*/
PIC_INLINE double RMSE(Image *ori, Image *cmp, bool bLargeDifferences = false, METRICS_DOMAIN type = MD_LIN)
{
return sqrt(MSE(ori, cmp, bLargeDifferences, type));
}