## 5 Decision-Making and Other Practical Cases  
Supposing that prediction has been carried out for a given image, one obtains \(\Delta PSNR\) or \(\Delta PSNR-HVS-M\) or both. A question is what to do further? There can be different options--to rely only on one parameter (rather than one of two) or on both. The choice is very heuristic without an obvious preference (e.g., special studies with customers assessing the compressed image quality can be carried out to obtain a reliable answer). We propose the following practical algorithm:  
1. If \(\Delta PSNR+\Delta PSNR-HVS-M\) > 1 dB, consider that an OOP exists and compress an image using Q\({}_{\text{OOP}}\);
2. If \(-1\) dB \(\leq\Delta PSNR+\Delta PSNR-HVS-M\leq 1\) dB, consider that an OOP might exist and use Q\({}_{\text{OOP}}\)-1 (but not less than 28) to avoid oversmoothing;
3. If \(\Delta PSNR+\Delta PSNR-HVS-M\leq-1\) dB, use Q = 28 to provide invisibility of introduced distortions and the possibility of noise removal in it by applying post-filtering to the decompressed images. An example in Figure 13 corresponds to case 3. An example for situation 1 is presented in Figure 15. The positive effect of a lossy compression is seen, especially in an image's homogeneous regions where the noise removal is obvious. Finally, an example for situation 2 is demonstrated in Figure 16. In this case, the positive effect of noise suppression can be noticed as well; however, edge/detail smearing takes place too. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Image**} & \multirow{2}{*}{**AWGN Variance**} & \multicolumn{3}{c}{**AGU**} & \multicolumn{3}{c}{**BPG**} \\ \cline{2-7}  & & **PSNR (dB)** & **CR** & **MS-SSIM** & **PSNR (dB)** & **CR** & **MS-SSIM** \\ \hline \multirow{3}{*}{Frisco} & 64 & 36.05 & 31.50 & 0.972 & 36.83 & 32.84 & 0.977 \\  & 100 & 34.84 & 38.36 & 0.965 & 35.68 & 41.17 & 0.972 \\  & 196 & 33.09 & 54.93 & 0.949 & 33.83 & 56.63 & 0.960 \\ \hline \multirow{2}{*}{Fr01} & 100 & 28.26 & 9.33 & 0.966 & 29.66 & 10.09 & 0.976 \\  & 196 & 26.66 & 14.3 & 0.950 & 27.78 & 14.3 & 0.963 \\ \hline \multirow{2}{*}{Aerial} & 100 & 28.29 & 8.87 & 0.985 & 29.63 & 9.54 & 0.976 \\  & 196 & 26.57 & 13.03 & 0.946 & 27.67 & 13.04 & 0.962 \\ \hline \multirow{2}{*}{Airfield} & 100 & 27.30 * & 7.69 & 0.951 * & 28.25 & 10.52 & 0.956 \\  & 196 & 25.79 & 12.35 & 0.927 * & 26.69 & 12.34 & 0.938 \\ \hline \hline \end{tabular}
* OOP is absent.  
\end{table} 
Table 2: Obtained simulation data. If situation 1 is falsely recognized as situation 2 or vice versa, practically nothing happens since the recommended \(Q\) values differ only by unity. If situation 2 is falsely recognized as situation 3, it is not a problem since a \"careful\" compression is carried out (by the expense of a smaller CR). If situation 3 is falsely recognized as situation 2, a slightly more smearing of the compressed image can be observed (however, the CR is larger). Finally, situations 1 and 3 are practically never misclassified. Figure 15: Noisy image Frisco (**a**) and the same image compressed in OOP (**b**), noise variance equal to 196, and the noise-free image (**c**). Thus, the fully automatic procedure for a given noisy image is as follows:  
1. Estimate the noise variance by some blind method of a noise variance assessment (if the noise variance is not known in advance);
2. Calculate \(Q_{\text{COOP}}\) according to (6); calculate \(P_{2\sigma}\) according to (7);
3. Calculate \(\Delta PSNR\) and \(\Delta PSNR-HVS-M\) using the expressions and parameters in Table 1;
4. Make a decision on the recommended \(Q_{\text{COOP}}\) according to the algorithm described above;
5. Carry out a compression using the recommended \(Q_{\text{COOP}}\). As it has been stated in Section 2, AWGN can be a simplified noise model for which the noise variance or standard deviation can be estimated in a blind manner (automatically) [50,51]. A more general case is the signal-dependent noise model [52].