**2. Related Works**

Qualitative assessment for compressed aerial images plays a vital role in identifying the quality of different features of interest after the compression like forests, marshes, shrubs, roads, buildings, water bodies, and others. The task is to identify the important visual features that were lost during lossy compression. The features can be characterized by generalizations like shape, size, density, color tone, texture [22,23]. Various land covers, like vegetation, sand, or water, have distinct textures and colors in the aerial images. Information on the change of the colors and textures can be calculated using global [20] and local [20,23] statistical parameters: first-order histogram-based global statistics, like standard deviation, mean, or second-order Grey Level Co-occurrence Matrix (GLCM)-based local statistics like contrast, homogeneity, entropy, and other. Textural or color changes can be evaluated without prior image processing, and compared to the other features like shape, size, density. The effect of lossy compression on the region color is usually calculated using statistics of the image color components before and after compression obtaining color components after image conversion to the appropriate color spaces [20]. Qualitative evaluation of color changes after compression can be performed using prior image processing—segmentation by color [24,25]—and comparing the quality of segmentation before and after compression, where the original image segmentation results serve as ground truth. The color RGB images of the remote sensing are obtained by assigning a specific multispectral band to each RGB channel. The obtained color of the object is the combination of radiometric resolution (RS), or the number of bits obtained for each band. The RS range depends on the possibility to collect values, based on the sensitivity and range of the instrumentation. As edge detection is closely related to the image density [26,27], the qualitative evaluation of edge detection after image compression can be used to assess the change of compressed and original image density. Segmentation, edge detection, morphological, and other image processing and analysis methods are applied to extract the regions of the image. After the image regions are separated, their shape and size can be evaluated. Mostly the general objective image quality assessment (IQA) metrics are used to evaluate the degradations of the compressed images: mean square error (MSE) [8,19], Peak Signal to Noise Ratio (PSNR) [8,9,13,19], Structural Similarity Index (SSIM) [8,19], multiscale SSIM (MS-SSIM) [8,19], Visual Information Fidelity (VIF) [19], etc. Some authors derived new methods to evaluate compression quality [28,29]. There were attempts to include more parameters for the comparative analysis and evaluation of the compression algorithms, including texture measures [20]. In [13,16,18], the authors included compression speed and compression ratios to evaluate the performance of appropriate algorithms. The change of land cover pattern after lossy compression influences the proper detection of distinct areas and their boundaries. The impact of lossy compression on the content of remote sensing images usually occurs at higher compression ratios and depends on the compression algorithms. In [30], the effect of lossy compression was evaluated for edge detection, segmentation [31,32], and classification [32,33]. The visual features' degradation in areal images compressed using lossy algorithms is related to the image content and resolution [34]. The effect of lossy compression on the processed result and quality of the compressed images can be evaluated using subjective metrics like Mean Opinion Score (MOS), but it is not always effective. Visual data in the relevant application areas can be collected, compressed for easier transmission, saving storage space, and later reviewed by the inspectors or processed by the appropriate methods [35]. In these cases, it should be useful to find the best solution for image lossy compression implementation in hardware.

We aim to manage the qualitative rating process of lossy compression by the set of qualitative parameters such as general image quality metrics, change of color (using first-order histogram-based statistics), change of texture (using second-order GLCM-based statistics), and subjective evaluation (using MOS). These qualitative parameters were applied both for the ranking of lossy compression algorithms and the decision making about the acceptable threshold of visual distortions in the lossy compressed image. A weighted combination of different qualitative parameters could be used to evaluate the

effect of lossy compression on the image content more precisely. This approach also provides the possibilities to solve different subtasks, either altering the weights or the set of aspects.

The impact of the lossy compression process was analyzed using a set of qualitative parameters, considered as criteria in our multi-criteria decision-making methodology. The MCDM approach was successfully applied in a broad spectrum of image processing areas: improvement of edge detection [36] and segmentation [37] of images, selection of edge detection algorithms for satellite images [27]. The MCDM was used as an important technique in sustainability engineering [38,39], as a solution for various complex tasks based on the assessment of variants. Direct determination for qualitative parameters' weighting and WASPAS methods were chosen as efficient decision-making tools. These methods were not applied for the qualitative rating of lossy compression by a set of aspects before our research. WASPAS is capable of providing a higher accuracy result compared to the weighted product model (WPM), and the weighted sum model (WSM) methods as it is the combination of both methods [40].
