# Improved Combined Metric for Automatic Quality Assessment of Stitched Images
## 2 The Quality Evaluation of Stitched Images
### The Overview of Quality Metrics  
Methods for stitched image quality assessment proposed before the development of the ISIQA dataset focused mainly on the lossy compression artifacts and partial distortions specific to panoramic images. Unlike in general-purpose IQA, reference-stitched images without any distortions are often unavailable; hence, the most typical full-reference (FR) approach, which is based on the comparison of two images (\"pristine\" image and the distorted one), cannot be directly applied. On the other hand, a purely no-reference (NR) approach may also be troublesome, as such metrics are usually less correlated with MOS values. Nevertheless, a reasonable approach for the quality evaluation of stitched images may be the comparison of some features extracted for the constituent and finally stitched images. Such an approach may be considered a compromise between the FR and NR IQA methods. One such method known as the Multi-view Image Quality Measure (MIQM), which was proposed by [PERSON] and [PERSON] [29,30], consists of luminance, contrast, spatial motion, and edge-based structure components. A simplified version of some parts of this metric, namely the luminance and contrast index, and the edge-based structural index, was used in the construction of the combined metric and experiments presented in a later section of the paper (Section 3). A more detailed description of this simplified implementation can be found in [31]. [PERSON] et al. [32] proposed the HFI_SSIM metric utilizing the popular Structural Similarity (SSIM) method [33] for comparisons of the high-frequency data in the overlapping regions of constituent and stitched images together with low-frequency information used for the assessment of the photometric panorama quality using the intensity magnitude ratio measures and spectral angle mapper. [PERSON] and [PERSON] [34] combined the Feature Similarity (FSIM) metric [35] and the improved Color Image Difference (iCID) measure [36] in their classification framework of color correction methods for image stitching. The problem of color balancing and correction in image and video stitching was examined in [37]. [PERSON] et al. [38] proposed a no-reference metric called the NRQQA (No-Reference Quantitative Quality Assessment), which was composed of three features: a multi-directional gradient, phase congruency, and saliency. This method focused on the automatically located overlapping regions assuming the presence of structural discontinuity and ghosting only in those areas. Unfortunately, its authors did not present an analysis of its agreement with the subjective evaluations of the use of the available image datasets. The authors of the ISIQA dataset also proposed an interesting quality metric for panoramic images, referred to as the Stitched Image Quality Evaluator (SIQE) [20]. This metric was based on the use of 36 features designed to capture the differences and deviations in the statistics of images as a result of degraded quality. The first group of features is sensitive to structural changes observed as changed, added, or removed edges as well as the presence of blur. The second group represents the ghosting artifacts caused by the variations in the spatial correlations. Then, the weighted average of features calculated for the constituent and stitched image 100 \(\times\) 100 pixel patches is determined and the final quality prediction is conducted for feature differences using support vector regression. The three main groups of features that are the inputs for the SVM regressor are the 12 shape parameters of the subband coefficients obtained by fitting a Generalized Gaussian Distribution (GGD), 12 eigenvalues of the bivariate distribution obtained from the horizontal neighbors by fitting a bivariate Gaussian Mixture Model (GMM) to the horizontally adjacent subband coefficients, and 12 eigenvalues of the bivariate distribution obtained from the vertical neighbors by fitting a bivariate GMM to the vertically adjacent subband coefficients.