Application of Structural Similarity Based Metrics for Quality Assessment of 3D Prints

Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

“…The decrease in the number of regions to 64 causes the improper classification of two yellow samples Although the presented method has been verified for 78 flat surfaces manufactured from two types of materials (PLA and ABS), the application of the presented approach can be easily extended also for some other shapes (e.g. convex surfaces) since there are no assumptions related to the presence of straight lines in contrast to, e.g., the use of Hough transform or local similarity metrics [21,22]. Since the proposed metric is based on image entropy, which is independent on the orientation of the lines (as, e.g., the assessment based on Hough transform), it may be applied for curved surfaces similarly as for the flat ones (with necessary adjustment of the classification threshold).…”

“…Another direction of our further research is the combination of the method proposed in the paper with some additional features which were investigated in earlier papers, e.g. based on texture analysis [19], Hough transform [21] or some image quality assessment methods based on the mutual structural similarity [22]. Such combination of features might be useful, especially for uncontrolled lighting conditions improving the robustness of the method.…”

“…Another investigated approach is based on the use of some full-reference image quality assessment (IQA) methods, such as structural similarity (SSIM) and its modifications as well as feature similarity (FSIM) [21,22]. Since the use of full-reference methods, based on the comparison with the reference image, would require a calibrated model of the 3D-printed surface, we have proposed a solution based on the division of images into regions (equivalent to blocks, avoiding the use of the word "windows" due to a possible misunderstanding caused by the use of "sliding windows" in convolutional filters as well as similarity-based IQA methods, such as aforementioned SSIM or FSIM).…”

“…Results obtained for the mean-variance (MV)-based metric[26] and division into 64 fragments samples moderately low quality samples low quality samples high quality samples with cracks moderately high quality samples with cracks moderately low quality samples with cracks low quality samples with cracks phenomenon can be easily observed, e.g. for the brown samples (no [19][20][21][22][23][24][25][26][27][28],. in Figs.…”

Current Trends and Advances in Image Quality Assessment

“…In computer games the localization of distortions may be as much important as the overall evaluation, however some of existing metrics can be efficiently applied for this purpose [77]. Therefore, some datasets and metrics with marked and subjectively assessed local distortions, especially game artifacts, are of particular interest [78].…”