@InProceedings{IPC2021_MK,
author="Okarma, Krzysztof
and Kopytek, Mateusz",
editor="Chora{\'{s}}, Michal
and Chora{\'{s}}, Ryszard S.
and Kurzy{\'{n}}ski, Marek
and Trajdos, Pawe{\l}
and Peja{\'{s}}, Jerzy
and Hyla, Tomasz",
title="Application of Image Entropy Analysis for the Quality Assessment of Stitched Images",
booktitle="Progress in Image Processing, Pattern Recognition and Communication Systems",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="125--132",
doi="10.1007/978-3-030-81523-3_12",
abstract="Image stitching is one of the relevant operations useful in virtual reality as well as in remote sensing applications which is used for the generation of panoramic images. The quality of the obtained panoramic images may be decreased due to the presence of various factors, e.g., geometric distortions, blur, ghosting, or colour distortions. Since the distortion types specific for the stitched images are different than those that may be found in the databases used for general-purpose image quality assessment, the development of new quality metrics, as well as the testing image databases, are necessary. One of the recently proposed methods for the evaluation of the stitched images is based on the comparison of 36 features of the constituent and finally stitched images, including shape parameters of the Generalized Gaussian Distribution (GGD) and the eigenvalues of a bivariate distribution obtained from the Gaussian Mixture Model (GMM). Although this method, known as Stitched Image Quality Evaluation (SIQE), provides a high correlation with subjective scores for the ISIQA dataset developed by its authors, its verification has been originally made assuming the use of only 20{\%} of images for testing and 80{\%} for training. Nevertheless, a noticeably smaller correlation with subjective quality scores obtained for the whole dataset may be meaningfully increased, employing an additional image entropy analysis proposed in the paper. The obtained results are encouraging and the proposed extension of the SIQE metric increases significantly both the Pearson's Linear Correlation Coefficient and Rank-Order Correlations with Mean Opinion Scores provided in the ISIQA database.",
isbn="978-3-030-81523-3"
}