*4.2. Qualitative Evaluation of Aerial Image Lossy Compression*

The qualitative evaluation was performed using MATLAB. After lossy compression of images "img1", "img2", "img3", "img4", "img5", and "img6" all selected qualitative parameters were calculated and estimated using the original and reconstructed versions of the aerial images.

The first group of parameters—texture change of images after compression—includes the second-order statistics: contrast (Equation (2)), correlation (Equation (3)), homogeneity (Equation (4)), energy (Equation (5)), and entropy (Equation (6)). The GLCM-based statistics were calculated for the luminance component in the YCbCr space of the original and compressed images. The difference between them was used to evaluate texture changes. The size of the probability matrix *P* of the GLCM method was determined by the number of the original gray levels in the Y component. The smaller distances between the image pixel of interest and its neighboring pixel are used to capture local texture information. For 3000 × 3000 images with the resolution of 1 pixel = 0.5 m and for 750 × 750 images with resolution of 1 pixel = 2 m were taken different displacements (respectively, *d* = 1, 2, 3, 4, 5, 6, and *d* = 1) for six directions (*θ* = 0◦ , 45◦ , 90◦ , 135◦ , 180◦ , 225◦ , 270◦and 315◦ ) as the texture changes are to be assessed in the same area. The extra pixel in each direction is used to minimize the influence of pixel-shifting during image resizing. The mean of each GLCM's texture statistic was calculated to define final contrast, correlation, homogeneity, energy, and entropy measures used to evaluate changes after lossy compression. Calculated texture changes between the original and compressed images are presented in Figure 6a–e. The more significant difference between the qualitative parameters of textures of the original and distorted images, the higher impact of lossy compression was on the loss of image texture information. As seen from Figure 6a,e, changes of texture contrast and entropy commonly increase with increasing compression ratios for the selected algorithms. However, statistical changes in textures increase in the negative direction for correlation, homogeneity, and energy as the compression ratios increase (Figure 6b–d). Table 2 in the first five rows shows the same results for the calculated qualitative texture parameters, respectively, *C*1, *C*2, *C*3, *C*4, and *C*<sup>5</sup> but only for the "img1". The differences between the qualitative parameters of textures of the original and distorted images slightly decrease in some cases from the lower compression ratios to the higher as presented in Figure 6c,e, respectively, for homogeneity and entropy changes using ECW lossy compression from 75:1 to 100:1 compression ratios for "img5".

The second group of parameters—color change of images after compression—includes the first-order statistics: mean and standard deviation of Cb and Cr components. Calculated color changes between the original and the compressed images are presented in Figure 6g,h,i,j. The more significant the difference between the original and distorted images' qualitative color parameters, the higher the impact of lossy compression on the distortions of color information. Changes of chrominance (mean and standard deviation) commonly increase with increasing compression ratios for the selected algorithms. Rows from 7 to 10 in Table 2 show the calculated results of the qualitative texture parameters *C*7, *C*8, *C*9, and *C*<sup>10</sup> for the "img1". For JPEG lossy compression ratios from 75:1 to 100:1, the change of the mean for the Cb component considerably decreases for "img6", as presented in Figure 6g.

The third group of parameters—supervised objective IQA metrics—includes PSNR for YCbCr image (Equation (8)), PSNR-HVS-M for YCbCr image (Equation (10)), SSIM for Y component (Equations (13) and (14)), MS-SSIM for Y component (Equation (15)). The supervised objective quality metrics compare the compressed images with the original, and the better image quality is indicated by a higher score (Figure 6l–o). The same results of IQA metrics for the "img1" are presented in Table 2, rows 12 to 15, respectively, including criteria *C*12, *C*13, *C*14, and *C*15. IQA metrics decrease with increasing compression ratios for the selected algorithms for all images.

**Figure 6.** Influence of lossy compression with selected compression ratios to the quality of the reconstructed different aerial images after compression. Distinct colors represent aerial images "img1", "img2", "img3", "img4", "img5" and "img6". Different line types represent lossy compression algorithms JPEG2000, ECW, JPEG. Each figure shows the results of the selected qualitative parameter (criterion) for the six images compressed with different algorithms: (**a**) *C*1—contrast change for Y component; (**b**) *C*2—correlation change for Y component; (**c**) *C*3—homogeneity change for Y component; (**d**) *C*4—energy change for Y component; (**e**) *C*5—entropy change for Y component; (**f**) *C*6—MOS for RGB image texture; (**g**) *C*7—change of the mean of Cb component; (**h**) *C*8—change of the mean of Cr component; (**i**) *C*9—change of the standard deviation of Cb component; (**j**) *C*10—change of the standard deviation of Cr component; (**k**) *C*11—*MOS* for RGB image colors; (**l**) *C*12—SSIM for Y component; (**m**) *C*13—MS-SSIM for Y component; (**n**) *C*14—PSNR for YCbCr image; (**o**) *C*15— PSNR-HVS-M for YCbCr image; (**p**) *C*16—MOS for RGB image artifacts; (**q**) legends for marking the separate algorithms in each figure; (**r**) legends for marking the separate images in each figure.

*Symmetry* **2021**, *13*, 273



The fourth group of parameters—subjective evaluations of RGB images after compression —includes MOS values in the range from 1 to 10 using a 10-point Likert scale [77] for the quality of image textures, colors, and artifacts after compression. The meaning of the qualitative parameters *C*6, *C*11, *C*16, and a rating scale was explained to the 15 students of Information Technologies. They were provided with a questionnaire (see Appendix A) and an individual blank response table (Table 2), excluding all rows except the sixth, eleventh, and sixteenth. The respondents were asked to fill the response tables for the six images. The assessments' mean values were calculated and included as criteria *C*6, *C*11, and *C*16. Estimated MOS values for textures, colors, general artifacts after lossy compression are presented in both Table 2 and Figure 6f,k,p.

The threshold for the acceptable visible distortions in aerial images reconstructed after each lossy compression was estimated visually by five experts from Vilnius Gediminas technical university, Department of Graphical systems with at least 15 years of experience in image processing. These experts also present their opinion concerning the image visual criteria weights considered in Section 3.4. The qualitative parameters from acceptable compression ratios for each algorithm and image were taken considering their groups texture, color, IQA. Three threshold alternatives—JPEG2000-A01, ECW-A02, JPEG-A03 were constructed to evaluate acceptable visible distortions for each algorithm (Table 2, *A*13, *A*14, *A*15). The threshold alternatives help to decide at which compression ratios of selected lossy compression algorithms are aerial images' distortions visually acceptable.
