Colourlab Image Database: Geometric Distortions

2021 ◽  
Vol 2021 (29) ◽  
pp. 258-263
Author(s):  
Marius Pedersen ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Performance ◽  
Image Database ◽  
Quality Metrics ◽  
Current Image ◽  
Geometric Distortions ◽  
Image Quality Metrics ◽  
Objective Metrics ◽  
Reference Images

Over the years, a high number of different objective image quality metrics have been proposed. While some image quality metrics show a high correlation with subjective scores provided in different datasets, there still exists room for improvement. Different studies have pointed to evaluating the quality of images affected by geometrical distortions as a challenge for current image quality metrics. In this work, we introduce the Colourlab Image Database: Geometric Distortions (CID:GD) with 49 different reference images made specifically to evaluate image quality metrics. CID:GD is one of the first datasets which include three different types of geometrical distortions; seam carving, lens distortion, and image rotation. 35 state-ofthe-art image quality metrics are tested on this dataset, showing that apart from a handful of these objective metrics, most are not able to show a high performance. The dataset is available at <ext-link ext-link-type="url" xlink:href="http://www.colourlab.no/cid">www.colourlab.no/cid</ext-link>.

How Good is Too Good? A Subjective Study on Over Enhancement of Images

2021 ◽  
Vol 2021 (29) ◽  
pp. 83-88
Author(s):  
Sahar Azimian ◽  
Farah Torkamani Azar ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Contrast ◽  
Current Image ◽  
Low Contrast ◽  
Image Dataset ◽  
Long Time ◽  
Reference Images ◽  
Subjective Study ◽  
Contrast Image

For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.

Color image database for evaluation of image quality metrics

2008 ◽  
Author(s):  
N. Ponomarenko ◽  
V. Lukin ◽  
K. Egiazarian ◽  
J. Astola ◽  
M. Carli ◽  
...  
Keyword(s):  
Image Quality ◽  
Color Image ◽  
Image Database ◽  
Quality Metrics ◽  
Image Quality Metrics

scholarly journals Comparison of Objective Image Quality Metrics to Expert Radiologists’ Scoring of Diagnostic Quality of MR Images

2020 ◽  
Vol 39 (4) ◽  
pp. 1064-1072 ◽  
Author(s):  
Allister Mason ◽  
James Rioux ◽  
Sharon E. Clarke ◽  
Andreu Costa ◽  
Matthias Schmidt ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Metrics ◽  
Mr Images ◽  
Diagnostic Quality ◽  
Image Quality Metrics

scholarly journals Iterative Phase-Only Hologram Generation Based on the Perceived Image Quality

2019 ◽  
Vol 9 (20) ◽  
pp. 4457 ◽  
Author(s):  
Haining Yang ◽  
Daping Chu
Keyword(s):  
Image Quality ◽  
Mean Squared Error ◽  
Quality Metrics ◽  
Critical Element ◽  
Image Content ◽  
Quality Metric ◽  
Squared Error ◽  
Image Quality Metrics ◽  
Image Quality Metric

Image quality metrics are a critical element in the iterative Fourier transform algorithms (IFTAs) for the computer generation of phase-only holograms. Conventional image quality metrics such as root-mean-squared error (RMSE) are sensitive to image content and unable to reflect the perceived image quality accurately. This would have a significant impact on the calculation speed and the quality of the generated hologram. In this work, the structure similarity (SSIM) was proposed as an image quality metric in IFTAs due to its ability to predict the perceived image quality in the presence of the white Gaussian noise and its independence on the image content. This would enable IFTAs to terminate when further iterations would no longer lead to improvement in the perceived image quality. As a result, up to 75% of unnecessary iterations were eliminated by the use of SSIM as the image quality metric.

Overview of Image Quality Metrics with Perspective to Satellite Image Compression

2016 ◽  
Vol 24 ◽  
pp. 112-123
Author(s):  
S. Sanjith ◽  
R. Ganesan
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Satellite Image ◽  
Visual Quality ◽  
Quality Metrics ◽  
Human Beings ◽  
Picture Processing ◽  
Image Quality Metrics ◽  
Satisfactory Answer

Image Quality appraisal has been an exacting task in the field of image processing without any satisfactory answer so far. Image quality evaluation tries to quantify a visual quality, an amount of distortion in a given picture. These changes are an inescapable component of any digital picture processing. The correct method of valuing the human-perceived visual quality of the images is the assessment by the human beings. Unfortunately, this process is luxurious, time consuming and cannot be applied in real-time applications. Therefore, there is a demand for a computerized technique that would conceive of the human-perceived visual quality as close as possible. This survey presents an overview about different quality metrics used in-order to assess the image degradation. The few metrics studied are MSE, SNR, PSNR, SSIM, AD, MD, MAE, NK, VSNR, RMSE, UIQM, MSSSIM, FSSIM etc. The image quality metrics are verified with perspective to satellite pictures.

scholarly journals Evaluation of Organ Dose and Image Quality Metrics of Pediatric CT Chest-Abdomen-Pelvis (CAP) Examination: An Anthropomorphic Phantom Study

2021 ◽  
Vol 11 (5) ◽  
pp. 2047
Author(s):  
Nor Azura Muhammad ◽  
Zunaide Kayun ◽  
Hasyma Abu Hassan ◽  
Jeannie Hsiu Ding Wong ◽  
Kwan Hoong Ng ◽  
...  
Keyword(s):  
Image Quality ◽  
Multidetector Ct ◽  
Organ Dose ◽  
Quality Metrics ◽  
Ct Scanner ◽  
Image Quality Metrics ◽  
Tube Current ◽  
Significant Difference ◽  
Noise Ratio ◽  
The Impact

The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p > 0.05) observed between the protocols and image metrics.

A simple method for evaluating image quality of screen-film system using a high-performance digital camera

2009 ◽  
Author(s):  
Naotoshi Fujita ◽  
Asumi Yamazaki ◽  
Katsuhiro Ichikawa ◽  
Yoshie Kodera
Keyword(s):  
Image Quality ◽  
High Performance ◽  
Digital Camera ◽  
Film System ◽  
Simple Method

Image-quality metrics for characterizing adaptive optics system performance

1997 ◽  
Vol 36 (26) ◽  
pp. 6583 ◽  
Author(s):  
Robert T. Brigantic ◽  
Michael C. Roggemann ◽  
Kenneth W. Bauer ◽  
Byron M. Welsh
Keyword(s):  
Image Quality ◽  
Adaptive Optics ◽  
System Performance ◽  
Quality Metrics ◽  
Image Quality Metrics ◽  
Adaptive Optics System
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