Q1 and Q2 {#sec:pardisc}
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It is illustrated that visualization of the image will significantly vary by changing only one parameter of the 2D-MSPC [@Mouats2015] and [@Rijal2015]. Figure \[fig:lena\] shows the visualization of a lena’s image by using 2D-MSPC, with different values of the cutoff point $c_o$. Based on information from Table \[table:par\], other parameters of the 2D-MSPC are fixed to: $k_o=2$; $\varepsilon=0.0001$; $N_o=4$; $O=6$; $\lambda_{\min o}=3$; $\eta_o=2.1$; and $g_o=10$, for $o=1,\dots, O$ and $n=1,\ldots,N_o$. Figure \[fig:lena-orig\] shows the lena’s grayscale image of lena that was used in this simulation. Figures \[fig:lena-co-p55\] and \[fig:lena-co-p10\] show the 2D-MSPC images for $c_o=0.55$ and $c_o=0.10$, respectively. It is seen that by decreasing the cutoff value, image features become increasingly detectable. There are several features that could not be detected with $c_o=0.55$ (Figure \[fig:lena-co-p55\]). This issue gives rise to questions Q1 and Q2.

[0.25]{} ![Visualization of a lena image by using the 2D-SMPC, with different values of the cutoff point $c_o$, while other parameters are fixed.[]{data-label="fig:lena"}](lena-orig "fig:"){width="\linewidth"}

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[0.25]{} ![Visualization of a lena image by using the 2D-SMPC, with different values of the cutoff point $c_o$, while other parameters are fixed.[]{data-label="fig:lena"}](lena-pc-co-p55 "fig:"){width="\linewidth"}

[0.25]{} ![Visualization of a lena image by using the 2D-SMPC, with different 