# A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces
## 2 Materials and Methods
### Mutual Image Similarity Based on Full-Reference IQA Metrics
The proposed SR-SIM metric utilized the specific visual saliency model designed to reflect the attention mechanisms and finally the perceived image quality. The local similarity maps between the compared images are calculated using the gradient modulus determined using the Scharr filter. Obtained spectralresidual visual saliency (SRVS) maps and gradient values are compared using the same formulas as those used in SSIM and FSIM metrics. [PERSON] et al. proposed the application of Gradient Similarity (GSIM) [40], also based on the mathematically similar formula to the SSIM metric, where the gradient values are determined using \(5\times 5\) pixels kernels, making it possible to measure both contrast and structure changes. Later, the idea of DCT Subbands Similarity (DSS) was proposed by [PERSON] et al. [41] where the structural changes in subbands of Discrete Cosine Transform were measured and appropriately weighted. Another approach proposed by [PERSON] et al. in 2016 is the use of Multiscale Contrast Similarity Deviation (MCSD) [42]. This metric also has relatively low computational complexity and high correlation with subjective quality scores determined for the most relevant IQA datasets. Its authors proposed the calculation of contrast similarity maps in three scales and further pooling of three contrast similarity deviations determined for those maps. On the other hand, two Analysis of Distortion Distribution-based metrics, namely ADD-SSIM and ADD-GSIM, were proposed by [PERSON] et al. [43] where a new pooling model was the main focus. Nevertheless, the authors analyzed distributions of distortion position, distortion intensity, frequency changes, and histogram changes as the main elements influencing the overall image quality. Since the proposed pooling scheme was used mainly for the SSIM and GSIM metrics, both these versions were used in our paper. The SSIM-like formula may also be applied for the comparison of various features obtained using some transforms, similarly to already mentioned CW-SSIM or DSS metrics. Another such example is known as Riesz transform and Visual contrast sensitivity-based feature Similarity index (RVSIM) [44], where the use of Riesz transform was preceded by the application of a Log-Gabor filter for the decomposition of reference and distorted images. The Riesz transform feature matrix was then combined with gradient magnitude similarity and weighted using the visual contrast sensitivity function (CSF). The combination of contrast maps and visual saliency, as an extension of the MCSD metric, was proposed by [PERSON] et al. [45], and is referred to as Contrast and Visual Saliency Similarity-Induced Index (CVSSI). An original idea for extending the structural similarity metric, known as SSIM4, was proposed by [PERSON] et al. [46] where the fourth multiplicative component of the metric reflects a predictability of image blocks, defined as a minimum mean squared error (MSE) between the block and its neighboring blocks. Color versions of metrics considered in this paper (CCSSIM, CSSIM4) were used in our experiments as well.