Abstract
No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores.
Highlights
Modern remote sensing (RS) systems produce an enormous number of images that are later used for many valuable applications such as ecological monitoring, agriculture, and urban planning [1,2,3]
Remote sensing images of a certain type can be intended for a particular purpose, image quality assessment (IQA) results can be in improper agreement with this purpose and criteria used
The best five combinations for the Noise and Actual” (NA) subset are presented in Table 3 together with the absolute values of the KROCC and PLCC values, Spearman’s correlation has been assumed as the objective function during the optimization
Summary
Modern remote sensing (RS) systems produce an enormous number of images that are later used for many valuable applications such as ecological monitoring, agriculture, and urban planning [1,2,3]. It is often supposed that all RS images are of high quality. This is not so due to many reasons. There are practical situations when full-reference metrics [12,13] can be employed for this purpose [8] This happens when there is an image that can be considered as “pristine” (reference) and, after processing (e.g., lossy compression), one has the corresponding distorted image that should be compared to the reference one using a certain quality metric where both traditional (such as Mean Square Error—MSE or Peak Signal-to-Noise Ratio—PSNR) or visual quality (e.g., Structural Similarity—SSIM [14,15]) metrics can be applied
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