scholarly journals Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

2021 ◽  
Vol 11 (16) ◽  
pp. 7470
Author(s):  
Altynay Kadyrova ◽  
Vlado Kitanovski ◽  
Marius Pedersen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Quality Metrics ◽  
Reference Image ◽  
Single Shot ◽  
Current State ◽  
Image Quality Metrics ◽  
Objective Quality ◽  
Multiple Shot ◽  
Set Up

Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics’ ability to work with 2.5D prints under limited conditions.

Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment

2021 ◽  
Author(s):  
Naima Merzougui ◽  
Leila Djerou
Keyword(s):  
Image Quality ◽  
Genetic Programming ◽  
Quality Assessment ◽  
Predictive Models ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Symbolic Regression ◽  
Quality Metrics ◽  
Reference Image ◽  
Image Quality Metrics

Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.

scholarly journals Evaluation of DISORDER: retrospective image motion correction for volumetric brain MRI in a pediatric setting

2020 ◽  
Author(s):  
Katy Vecchiato ◽  
Alexia Egloff ◽  
Olivia Carney ◽  
Ata Siddiqui ◽  
Emer Hughes ◽  
...  
Keyword(s):  
Image Quality ◽  
Motion Correction ◽  
Spin Echo ◽  
Brain Mri ◽  
Quality Metrics ◽  
Image Motion ◽  
Reference Image ◽  
Outlier Rejection ◽  
Image Quality Metrics ◽  
Correction Technique

Background and Purpose: Head motion causes image degradation in brain MRI examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MRI acquisitions. Material and Methods: We prospectively acquired brain MRI at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted Turbo Spin Echo and FLAIR, in 32 unsedated children, including 7 with epilepsy (age range 2-18 years). We implemented a novel motion correction technique: Distributed and Incoherent Sample Orders for Reconstruction Deblurring using Encoding Redundancy (DISORDER). For each subject and modality, we obtained 3 reconstructions: as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: Gradient Entropy (GE) and MPRAGE White Matter Homogeneity (WM-H). As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. Results: Both image quality metrics improved after motion correction for all modalities and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z=-3.164 MPRAGE, z=-2.066 TSE, z=-2.645 FLAIR, for all p<0.05). Conclusions: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least one sequence was improved by this approach, indicating the benefit of this technique in un-sedated children for both clinical and research environments.

scholarly journals Perceptually Motivated Method for Image Inpainting Comparison

2019 ◽  
Author(s):  
Иван Молодецких ◽  
Ivan Molodetskikh ◽  
Михаил Ерофеев ◽  
Mikhail Erofeev ◽  
Дмитрий Ватолин ◽  
...  
Keyword(s):  
Image Quality ◽  
Standard Method ◽  
Image Inpainting ◽  
Human Perception ◽  
Quality Metrics ◽  
Future Research ◽  
Image Quality Metrics ◽  
Objective Quality

The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the problem’s challenging nature: image-­inpainting algorithms strive for realism in the resulting images, but realism is a subjective concept intrinsic to human perception. Existing objective image-­quality metrics provide a poor approximation of what humans consider more or less realistic. To improve the situation and to better organize both prior and future research in this field, we conducted a subjective comparison of nine state-­of­-the­-art inpainting algorithms and propose objective quality metrics that exhibit high correlation with the results of our comparison.

scholarly journals Evaluation of 360° Image Projection Formats; Comparing Format Conversion Distortion Using Objective Quality Metrics

2021 ◽  
Vol 7 (8) ◽  
pp. 137
Author(s):  
Ikram Hussain ◽  
Oh-Jin Kwon
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Projection Methods ◽  
Quality Metrics ◽  
Two Dimensional ◽  
Format Conversion ◽  
Equal Area ◽  
Objective Quality ◽  
Perceptual Image Quality ◽  
Image Format

Currently available 360° cameras normally capture several images covering a scene in all directions around a shooting point. The captured images are spherical in nature and are mapped to a two-dimensional plane using various projection methods. Many projection formats have been proposed for 360° videos. However, standards for a quality assessment of 360° images are limited. In this paper, various projection formats are compared to explore the problem of distortion caused by a mapping operation, which has been a considerable challenge in recent approaches. The performances of various projection formats, including equi-rectangular, equal-area, cylindrical, cube-map, and their modified versions, are evaluated based on the conversion causing the least amount of distortion when the format is changed. The evaluation is conducted using sample images selected based on several attributes that determine the perceptual image quality. The evaluation results based on the objective quality metrics have proved that the hybrid equi-angular cube-map format is the most appropriate solution as a common format in 360° image services for where format conversions are frequently demanded. This study presents findings ranking these formats that are useful for identifying the best image format for a future standard.

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