# Improved Combined Metric for Automatic Quality Assessment of Stitched Images
## 2 The Quality Evaluation of Stitched Images
### The Verification Methodology of the Developed Metrics
Another dataset known as the Multi-Distortions Visual Attention Quality Dataset (MVAQD) containing 15 high-quality uncompressed omnidirectional images and their distorted versions obtained with five distortion types at four distortion levels, respectively, namely Gaussian blur, HEVC intra coding, JPEG compression, JPEG2000 compression, and white noise, was presented in [26]. All the images were assessed by 26 independent observers, leading to MOS values being provided in the MVAQD dataset. Nevertheless, similar to previous datasets, the distortions present in the images were not specific to panoramic images and can be found in many types of images. Hence, many \"traditional\" IQA methods demonstrate relatively good performance also for them. Therefore, such datasets were not used in the experiments presented in our paper. The most comprehensive dataset developed by [PERSON] and [PERSON] [20] is known as the Indian Institute of Science Stitched Image Quality Assessment (ISIQA). It contains 264 stitched images obtained from 26 sets of 4 or 5 constituent images representing various scenes, together with the aggregated results of 6600 subjective assessments. The observers used head-mounted displays and then around 25 human opinion scores were obtained for each image, further averaged, and expressed as MOS values for each of the 264 stitched images. One of the most important advantages of this database is the choice of distortions that are specific to stitched images, such as ghosting and blur, color distortion, and geometric distortion. It is the most relevant difference in comparison to lossy compression and was mainly considered in earlier studies for omnidirectional images,where some of the general-purpose IQA metrics and their modifications were verified for such types of images. One of the assumptions of the ISIQA database is the lack of motion; therefore, only static scenes representing buildings, gardens, and indoor and public places are included in the dataset. Therefore, it focuses on the evaluation of horizontal stitching and modeling the distortions introduced during the stitching process. The inventors of the dataset excluded potential issues during image capture since the Samsung S7 Edge smartphone camera was used for this purpose to ensure reasonably high quality of constituent images [20]. Hence, the horizontal resolution of the obtained stitched images is around 8000-10,000 pixels, and the vertical resolution is about 2000 pixels. Sample stitched images from 2 of 26 sets together with the constituent images are shown in Figures 1 and 2. Similar to the other types of images and video sequences where the datasets containing the subjective quality scores are available, the verification of the newly developed metrics is based on the calculation of the three main correlation coefficients between two 264-element sequences of scores (objective and subjective), i.e., calculated metrics and MOS values for all 264 images from the ISIQA dataset. The most popular correlation coefficient that reflects prediction accuracy is known as Pearson's \(r\) or PLCC (Pearson's Linear Correlation Coefficient). Assuming the objective metric \(Q\), it may be defined as  
\[r=\frac{\text{cov}(Q,M\text{OS})}{\sigma_{Q}\cdot\sigma_{M\text{OS}}}, \tag{1}\]  
being the covariance to the product of the standard deviation ratio. Figure 2: Sample constituent (top row) and stitched images from set #3 of ISIQA database with various types of distortions. The two other popular rank-order correlations, namely Spearman Rank-Order Correlation Coefficient (SROCC), referred to as \(\rho\), and Kendall Rank-Order Correlation Coefficient (KROCC), denoted as \(\tau\), reflect the prediction monotonicity. SROCC may be expressed as  
\[\rho=1-\frac{6\cdot\sum d_{i}^{2}}{n\cdot(n^{2}-1)}, \tag{2}\]  
where \(n\) stands for the number of images and \(d_{i}\) is the difference between the ranks of the same images in two sequences sorted according to their objective (Q) and subjective (MOS) quality scores.