## 2 Materials and Methods
### Non-Reference Quality Assessment Metrics
#### 2.3.1 Quality Metrics
The PIQE index calculates the non-reference quality score for an image based on the following steps presented in Table 1.  
**Input:** Radargram after standard pre-processing  
Compute MSCN coefficient for each pixel in the image:  
MSCN = ComputeMSCN(I)  
Divide the input radargram into non-overlapping blocks of size 32-by-32:  
Blocks = DivideIntoBlocks(I, BlockSize = 32 \(\times\) 32)  
Identify high spatially active blocks based on the variance of the MSCN coefficients. For each block in Blocks:  
Calculate variance of MSCN coefficients  
If Variance of MSCN > Threshold  
Mark block as high spatially active  
Generate activityMask using the identified high spatially active blocks:  
activityMask = GenerateActivityMask(Blocks, HighSpatialActivity)  
Evaluate distortion due to blocking artifacts and noise using the MSCN coefficients. For each block in Blocks:  
Evaluate distortion using MSCN coefficients  
Assess blocking artifacts and Gaussian noise  
Classify the blocks using Threshold criteria as distorted and undistorted blocks:  
ClassifyBlocks(Blocks, ThresholdCriteria)  
Generate noticeableArtifactsMask from the distorted blocks  
Compute the PIQE score for input image  
PIQE_score = MeanScore(DistortedBlocks)  
**Output:** PIQE value, Quality of image  
**Laplacian Variance**  
The value of the Laplacian operator variance is a measure of the scattering of Laplacian results in the whole image. For blurred images, the value of the variance will be low, as a result of slight changes in pixel intensity and less marked edges. On the other hand, in sharpened images, the values of the Laplacian variance are higher, as major changes in pixel intensity in the areas of edges lead to more differentiated results. The value of variance may be calculated from Equation (5):  
\[\sigma=\frac{1}{N}{\sum\limits_{i=1}^{N}{\sum\limits_{j=1}^{N}{\left|{p(i,j)-\mu }\right|}}}, \tag{5}\]  
\begin{table}
\begin{tabular}{l} \hline \hline
**Input:** Radargram after standard pre-processing \\ \hline Compute MSCN coefficient for each pixel in the image:  
MSCN = ComputeMSCN(I)  
Divide the input radargram into non-overlapping blocks of size 32-by-32:  
Blocks = DivideIntoBlocks(I, BlockSize = 32 \(\times\) 32)  
Identify high spatially active blocks based on the variance of the MSCN coefficients. For each block in Blocks:  
Calculate variance of MSCN coefficients  
If Variance of MSCN > Threshold  
Mark block as high spatially active  
Generate activityMask using the identified high spatially active blocks:  
activityMask = GenerateActivityMask(Blocks, HighSpatialActivity)  
Evaluate distortion due to blocking artifacts and noise using the MSCN coefficients. For each block in Blocks:  
Evaluate distortion using MSCN coefficients  
Assess blocking artifacts and Gaussian noise  
Classify the blocks using Threshold criteria as distorted and undistorted blocks:  
ClassifyBlocks(Blocks, ThresholdCriteria)  
Generate noticeableArtifactsMask from the distorted blocks  
Compute the PIQE score for input image  
PIQE_score = MeanScore(DistortedBlocks)  
**Output:** PIQE value, Quality of image  
**Laplacian Variance**  
The value of the Laplacian operator variance is a measure of the scattering of Laplacian results in the whole image. For blurred images, the value of the variance will be low, as a result of slight changes in pixel intensity and less marked edges. On the other hand, in sharpened images, the values of the Laplacian variance are higher, as major changes in pixel intensity in the areas of edges lead to more differentiated results.