# Improved Combined Metric for Automatic Quality Assessment of Stitched Images
## 2 The Quality Evaluation of Stitched Images  
### The Verification Methodology of the Developed Metrics  
The quality of stitched images depends on several factors, although these are different to those of the general-purpose image quality assessment (IQA). Some other types of distortions may also be expected that may be caused by the improper choice of image features, incorrect blending and interpolation, differing lighting conditions in constituent images, etc., rather than, e.g., the presence of noise or lossy compression. Some other sources of visible errors may be geometrical distortions as a result of lens imperfections, or an overlapping region that is too small leading to a small number of detected keypoints. Capturing the constituent images during camera motion may result in motion blur or ghosting artifacts. These types of artifacts can be found in the images from the Indian Institute of Science Stitched Image Quality Assessment (ISIQA) dataset [20] used in our experiments. Nevertheless, general-purpose IQA metrics developed for natural images cannot be efficiently applied to panoramic images and new metrics dedicated to stitched images are necessary. Although great progress in image quality assessment has been made during recent years, one of its limitations has been the availability of image quality datasets containing reference images and their degraded versions together with the results of subjective evaluations by human observers, which are usually expressed as Mean Opinion Score (MOS) values. The use of several such databases makes it possible to conduct a cross-database validation for the newly proposed metrics. A further development in larger datasets additionally allows the use of deep Convolutional Neural Networks (CNNs) for this purpose. Nevertheless, the development of new quality evaluation methods for stitched images is limited by the availability of databases that require a lot of effort related to the preparation of images and collection of subjective evaluation scores. Therefore, the applicability of popular data-driven CNN methods for this purpose is significantly limited, as stated by [PERSON] et al. [21], who emphasized the necessity of training using a great number of images to avoid potential overfitting. A relatively large-scale dataset for omnidirectional image quality assessment known as OIQA was proposed by [PERSON] et al. [22], which contains 16 source images and 320 distorted images assessed by twenty subjects. They have been degraded by four commonly met distortion types: JPEG compression, JPEG2000 compression, Gaussian blur, and Gaussian noise. Nevertheless, these types of distortions are not specific for stitched images, and therefore most general-purpose IQA metrics demonstrate good performance for this dataset. Another example of an image dataset containing panoramic images was recently proposed, that is, the Color Correction-based Stitched Image Database (CCSID), containing 10 stitched images generated from 10 source panoramic image pairs with consistent colors, 400 stitched images generated from 50 panoramic image pairs with inconsistent colors, and 350 panoramic image pairs with color correction [23]. Nevertheless, this database focused only on color distortions and therefore its universality and diversity are substantially limited. Additionally, the experimental results obtained for this database presented in [23] demonstrate the different performances of various metrics in comparison to those obtained for the ISIQA dataset, which is discussed further. The two other datasets proposed previously, which also contain images degraded with limited types of distortions, were the Stitched Image Quality Assessment (SIQA) database proposed in [24] containing 816 stitched samples with perspective variations obtained from 12 different 3D scenes assessed by 28 viewers and the Compressed VR Image Quality Database (CVIQD2018) containing 6 source images and 528 compressed ones using three coding technologies (JPEG, H.264/AVC and H.265/HEVC) and assessed by 20 observers [25].