A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Oszust, Mariusz

doi:10.1515/msr-2016-0040

Cited by 7 publications

(8 citation statements)

References 42 publications

(79 reference statements)

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“…Some other approaches to multi-method fusion have also been proposed by some other researchers, e.g., based on the regression approach [50], also with the use of machine learning [51], application of neural networks [52], also for remote sensing images [44], or genetic algorithms [53]. Nevertheless, considering the assumed application for the quality assessment of the 3D printed surfaces during the manufacturing process, the use of a limited number of metrics combined using their weighted product is assumed according to the general formula:…”

Section: Proposed Approachmentioning

confidence: 99%

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

2020

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Quality assessment of the 3D printed surfaces is one of the crucial issues related to fast prototyping and manufacturing of individual parts and objects using the fused deposition modeling, especially in small series production. As some corrections of minor defects may be conducted during the printing process or just after the manufacturing, an automatic quality assessment of object’s surfaces is highly demanded, preferably well correlated with subjective quality perception, considering aesthetic aspects. On the other hand, the presence of some greater and more dense distortions may indicate a reduced mechanical strength. In such cases, the manufacturing process should be interrupted to save time, energy, and the filament. This paper focuses on the possibility of using some general-purpose full-reference image quality assessment methods for the quality assessment of the 3D printed surfaces. As the direct application of an individual (elementary) metric does not provide high correlation with the subjective perception of surface quality, some modifications of similarity-based methods have been proposed utilizing the calculation of the average mutual similarity, making it possible to use full-reference metrics without the perfect quality reference images, as well as the combination of individual metrics, leading to a significant increase of correlation with subjective scores calculated for a specially prepared dataset.

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Section: Proposed Approachmentioning

confidence: 99%

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

2020

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“…To compare the results for TID2008 database with previous attempts, the best LCSIM2 metric, consisting of 11 different single IQA metrics, achieves the value of PCC = 0.9202 (after nonlinear regression). Another family of regression based Similarity measures (rSIMs) was presented in [40] with the linear combination of 16 measures leading to PCC = 0.9218 for TID2018 dataset. Fig.…”

Section: B Fusion Of Iqa Metricsmentioning

confidence: 99%

“…The application of neural networks and some other machine learning algorithms for the fusion of IQA metrics was discussed by Barri et al [41], whereas the use of pairwise score differences to obtain the lasso regression Similarity measures (lrSIMs) was examined by Oszust [42]. Nevertheless, the obtained results were slightly worse than those presented in [40].…”

Section: B Fusion Of Iqa Metricsmentioning

confidence: 99%

Current Trends and Advances in Image Quality Assessment

Okarma¹

2019

ElAEE

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Image quality assessment (IQA) is one of the constantly active areas of research in computer vision. Starting from the idea of Universal Image Quality Index (UIQI), followed by well-known Structural Similarity (SSIM) and its numerous extensions and modifications, through Feature Similarity (FSIM) towards combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress. Nevertheless, regardless of new databases and the potential use of deep learning methods, some challenges remain still up to date. Some of the IQA metrics can also be used efficiently for alternative purposes, such as texture similarity estimation, quality evaluation of 3D images and 3D printed surfaces as well as video quality assessment.

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“…To the best of our knowledge, such optimization has not been yet carried out for available databases containing only images with multiple distortions. Previously developed combined metrics [5,6,8,11,12] concern only the singly distorted images.…”

Section: Introductionmentioning

confidence: 99%

Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images

Okarma

Lech

2021

Electronics

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In the recent years, many objective image quality assessment methods have been proposed by different researchers, leading to a significant increase in their correlation with subjective quality evaluations. Although many recently proposed image quality assessment methods, particularly full-reference metrics, are in some cases highly correlated with the perception of individual distortions, there is still a need for their verification and adjustment for the case when images are affected by multiple distortions. Since one of the possible approaches is the application of combined metrics, their analysis and optimization are discussed in this paper. Two approaches to metrics’ combination have been analyzed that are based on the weighted product and the proposed weighted sum with additional exponential weights. The validation of the proposed approach, carried out using four currently available image datasets, containing multiply distorted images together with the gathered subjective quality scores, indicates a meaningful increase of correlations of the optimized combined metrics with subjective opinions for all datasets.

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A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Cited by 7 publications

References 42 publications

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

Current Trends and Advances in Image Quality Assessment

Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images

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