# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
### Quality Assessment  
In general, QA of any print can be either subjective or objective, or a combination of both. For example, [PERSON] et al. [18] conducted a subjective experiment to identify meaningful image quality attributes for 2D color prints' QA. Their observers' task was to rate the quality of 2D color prints and state every quality attribute they used even if some quality attributes had little impact on the QA. The rating task considered seven scale levels, where a value of 1 meant that observers found the 2D color prints as the most pleasing, whereas a value of 7 meant the least pleasing. Afterwards, they validated the chosen quality attributes (color, sharpness, lightness, contrast, and artifacts) through another subjective experiment [19]. Regarding 2.5D print subjective QA, an experiment with observers was conducted to study the most used distinct attributes [20]. The observers judged the quality of 2.5D prints that were fabricated with a 2.5D printer. They were asked to rank the quality of 2.5D prints and describe the reasons for their ranking. The experiment comprised of two parts: first, when observers were not provided with the reference images and second, when the reference images were provided. The relevant attributes were proposed to be the top five most used distinct attributes in their experiment. These were color, sharpness, elevation, lightness, and naturalness. [PERSON] et al. [21] performed a subjective experiment with 2.5D prints and their goal was to find the effect of color on gloss. According to their results, color has no significant effect on gloss. Nevertheless, they suggested that color and gloss need to be taken into account for print QA. The objective QA aspect can involve a variety of metrics. There are many diverse sets of 2D IQMs depending on the application area, attributes, performance and accuracy level, and availability of reference images. According to the literature [1; 22; 23; 24; 25; 26], Human Visual System (HVS)-based metrics are better than those metrics without using a model of the HVS. Additionally, visual quality can be better predicted by the HVS-based metrics rather than simple pixel-based difference metrics [27]. There are Full-Reference (FR), reduced-reference, and No-Reference (NR) IQMs. Most of the IQMs are based on detecting distortions and predicting quality based on that. Due to the unavailability of reference images in practice, sometimes NR metrics are preferable. However, FR metrics can be more straightforward to detect distortions because of reference image availability [28]. Thus, there are pros and cons of each type of metric. Moreover, it is important to mention that these days it is becoming a trend to use deep learning or machine learning methods to create IQMs [29; 30; 31; 32]. For instance, [PERSON] et al. [28] created a FR metric that takes as input-filtered reference and distorted images and is based on deep neural networks. There are several frameworks for QA of 2D color prints/images using IQMs [1; 33; 34; 35]. For example, [PERSON] and [PERSON] [1] used Spatial CIELAB (SCIELAB) due to its often use as a reference metric, spatial hue angle metric because it combines two state-of-the-art metrics, adaptive bilateral filter due to the ability of bilateral filtering to simulate the HVS, Structural Similarity (SSIM) due to its common use and working scheme on local neighborhood, and other IQMs for QA of 2D color prints. They concluded that their results are both image and metric dependent. [PERSON] et al. [36] proposed potential approaches with advantages and disadvantages towards an IQM for 2.5D prints based on their reviewed literature for 3D prints' QA. [PERSON] et al. [3] performed objective QA of 2.5D printing process in height dimension in terms of fidelity and surface finish using modulation transfer function, mean absolute difference, and other metrics. The surface finish difference between real and ideal prints that might be perceived by observers was simulated through their created light-reflection model.