QwtRasterData and non-linearly sampled data

[DizzyMoods]

Hello. I want some suggestions on how to my code a bit more versatile. I altered the SpectrogramData example for a discrete series of XYZ data simply by coupling the x and y to the discrete values of my Data->ZData vector.

class SpectrogramData: public QwtRasterData
{
public:
...
    virtual double value(double x, double y) const
    {
        double dx, dy;
        dx = (Data->Xdata.last()-Data->Xdata.first())/(Data->Xdata.size()-1);
        dy = (Data->Ydata.last()-Data->Ydata.first())/(Data->Ydata.size()-1);
        int i = (x-Data->Xdata.first())/dx, j = (y-Data->Ydata.first())/dy;
        return Data->Zdata[j][i];
    }
...
}

class SpectrogramData: public QwtRasterData
{
public:
...
    virtual double value(double x, double y) const
    {
        double dx, dy;
        dx = (Data->Xdata.last()-Data->Xdata.first())/(Data->Xdata.size()-1);
        dy = (Data->Ydata.last()-Data->Ydata.first())/(Data->Ydata.size()-1);
        int i = (x-Data->Xdata.first())/dx, j = (y-Data->Ydata.first())/dy;
        return Data->Zdata[j][i];
    }
...
}

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This type of resampling works well if I am operating with linearly sampled data (XData and YData are equidistant). How can I improve this algorithm for a series of non-linearly scaled data (i.e. XData = [1 10 20 50 70 100 1000])?