Towards Subjective Consistency: An Effective Objective Quality Assessment Algorithm for Binary Image

2011 ◽  
Vol 474-476 ◽  
pp. 143-150
Author(s):  
Chun E Zhang ◽  
Fan Ci Guo ◽  
Ke Xiong
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
Binary Images ◽  
Opinion Score ◽  
Noise Point ◽  
Objective Quality ◽  
High Consistency ◽  
Image Assessment

Image quality assessment plays an important role in various image processing applications. One of the challenges to objectively assess image quality is how to design an effective scheme to achieve high consistency with the classic subjective image assessment criterion, Mean Opinion Score (MOS). This work presents a novel objective assessment algorithm for binary images by considering three factors which have great influences on visual quality of binary images, i.e., structural change caused by noise point, isolated noise points, and gathering noise points. Experimental results show that our algorithm can achieve effective objective assessment results with higher consistency with the MOS criterion.

Comprehensive image quality assessment via predicting the distribution of opinion score

2018 ◽  
Vol 78 (17) ◽  
pp. 24205-24222 ◽  
Author(s):  
Anan Liu ◽  
Jingting Wang ◽  
Jing Liu ◽  
Yuting Su
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Opinion Score

Subjective and objective evaluation of image quality in biplane cerebral digital subtraction angiography following significant acquisition dose reduction in a clinical setting

2016 ◽  
Vol 9 (3) ◽  
pp. 297-301 ◽  
Author(s):  
Amir R Honarmand ◽  
Ali Shaibani ◽  
Tamila Pashaee ◽  
Furqan H Syed ◽  
Michael C Hurley ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Standard Dose ◽  
Objective Assessment ◽  
Objective Evaluation ◽  
Side Branch ◽  
Image Series ◽  
Significant Difference

ObjectiveDifferent technical and procedural methods have been introduced to develop low radiation dose protocols in neurointerventional examinations. We investigated the feasibility of minimizing radiation exposure dose by simply decreasing the detector dose during cerebral DSA and evaluated the comparative level of image quality using both subjective and objective methods.MethodsIn a prospective study of patients undergoing diagnostic cerebral DSA, randomly selected vertebral arteries (VA) and/or internal carotid arteries and their contralateral equivalent arteries were injected. Detector dose of 3.6 and 1.2 μGy/frame were selected to acquire standard dose (SD) and low dose (LD) images, respectively. Subjective image quality assessment was performed by two neurointerventionalists using a 5 point scale. For objective image quality evaluation, circle of Willis vessels were categorized into conducting, primary, secondary, and side branch vessels. Two blinded observers performed arterial diameter measurements in each category. Only image series obtained from VA injections opacifying the identical posterior intracranial circulation were utilized for objective assessment.ResultsNo significant difference between SD and LD images was observed in subjective and objective image quality assessment in 22 image series obtained from 10 patients. Mean reference air kerma and kerma area product were significantly reduced by 61.28% and 61.24% in the LD protocol, respectively.ConclusionsOur study highlights the necessity for reconsidering radiation dose protocols in neurointerventional procedures, especially at the level of baseline factory settings.

scholarly journals An Improved SPSIM Index for Image Quality Assessment

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 518
Author(s):  
Mariusz Frackiewicz ◽  
Grzegorz Szolc ◽  
Henryk Palus
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Space ◽  
Similarity Index ◽  
Mean Opinion Score ◽  
Assessment Process ◽  
Mean Deviation ◽  
Strongly Correlated ◽  
Opinion Score

Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

scholarly journals Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1525
Author(s):  
Krzysztof Okarma ◽  
Wojciech Chlewicki ◽  
Mateusz Kopytek ◽  
Beata Marciniak ◽  
Vladimir Lukin
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Subjective Evaluation ◽  
General Purpose ◽  
Sensing Applications ◽  
Objective Quality ◽  
Panoramic Images ◽  
Remote Sensing Applications

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

scholarly journals Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead

2021 ◽  
Vol 2021 (1) ◽  
pp. 1-4
Author(s):  
Seyed Ali Amirshahi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Machine Learning Techniques ◽  
Short Paper ◽  
Subjective Quality Assessment ◽  
Learning Techniques ◽  
Objective Quality

Quality assessment of images plays an important role in different applications in image processing and computer vision. While subjective quality assessment of images is the most accurate approach due to issues objective quality metrics have been the go to approach. Until recently most such metrics have taken advantage of different handcrafted features. Similar (but with a slower speed) to other applications in image processing and computer vision, different machine learning techniques, more specifically Convolutional Neural Networks (CNNs) have been introduced in different tasks related to image quality assessment. In this short paper which is a supplement to a focal talk given with the same title at the London Imaging Meeting (LIM) 2021 we aim to provide a short timeline on how CNNs have been used in the field of image quality assessment so far, how the field could take advantage of CNNs to evaluate the image quality, and what we expect will happen in the near future.

Performance Evaluation of Color Retinal Image Quality Assessment in Asymmetric Channel VQ Coding

2013 ◽  
Vol 4 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Agung W. Setiawan ◽  
Andriyan B. Suksmono ◽  
Tati R. Mengko ◽  
Oerip S. Santoso
Keyword(s):  
Image Quality ◽  
Blood Vessel ◽  
Quality Assessment ◽  
Image Coding ◽  
Channel Coding ◽  
Retinal Image ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
The Other ◽  
Retinal Blood Vessel

The RGB color retinal image has an interesting characteristic, i.e. the G channel contains more important information than the other ones. One of the most important features in a retinal image is the retinal blood vessel structure. Many diseases can be diagnosed based on in the retinal blood vessel, such as micro aneurysms that can lead to blindness. In the G channel, the contrast between retinal blood vessel and its background is significantly high. The authors explore this retinal image characteristic to construct a more suitable image coding system. The coding processes are conduct in three schemes: weighted R channel, weighted G channel, and weighted B channel coding. Their hypothesis is that allocating more bits in the G channel will improve the coding performance. The authors seek for image quality assessment (IQA) metrics that can be used to measure the distortion in retinal image coding. Three different metrics, namely Peak Signal to Noise Ratio (PSNR), Structure Similarity (SSIM), and Visual Information Fidelity (VIF) are compared as objective assessment in image coding and to show quantitatively that G channel has more important role compared to the other ones. The authors use Vector Quantization (VQ) as image coding method due to its simplicity and low-complexity than the other methods. Experiments with actual retinal image shows that the minimum value of SSIM and VIF required in this coding scheme is 0.9940 and 0.8637.

scholarly journals Benchmarking of objective quality metrics for HDR image quality assessment

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
Philippe Hanhart ◽  
Marco V. Bernardo ◽  
Manuela Pereira ◽  
António M. G. Pinheiro ◽  
Touradj Ebrahimi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Quality Metrics ◽  
Objective Quality ◽  
Hdr Image

scholarly journals Subjective and Objective Quality Assessment of Swimming Pool Images

2022 ◽  
Vol 15 ◽  
Author(s):  
Fei Lei ◽  
Shuhan Li ◽  
Shuangyi Xie ◽  
Jing Liu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Swimming Pool ◽  
Target Area ◽  
Quality Models ◽  
Objective Quality ◽  
Objective Quality Assessment ◽  
Pool Image

As the research basis of image processing and computer vision research, image quality evaluation (IQA) has been widely used in different visual task fields. As far as we know, limited efforts have been made to date to gather swimming pool image databases and benchmark reliable objective quality models, so far. To filled this gap, in this paper we reported a new database of underwater swimming pool images for the first time, which is composed of 1500 images and associated subjective ratings recorded by 16 inexperienced observers. In addition, we proposed a main target area extraction and multi-feature fusion image quality assessment (MM-IQA) for a swimming pool environment, which performs pixel-level fusion for multiple features of the image on the premise of highlighting important detection objects. Meanwhile, a variety of well-established full-reference (FR) quality evaluation methods and partial no-reference (NR) quality evaluation algorithms are selected to verify the database we created. Extensive experimental results show that the proposed algorithm is superior to the most advanced image quality models in performance evaluation and the outcomes of subjective and objective quality assessment of most methods involved in the comparison have good correlation and consistency, which further indicating indicates that the establishment of a large-scale pool image quality assessment database is of wide applicability and importance.

scholarly journals Picture-wise just noticeable difference prediction model for JPEG image quality assessment

2022 ◽  
Vol 70 (1) ◽  
pp. 62-86
Author(s):  
Boban Bondžulić ◽  
Boban Pavlović ◽  
Nenad Stojanović ◽  
Vladimir Petrović
Keyword(s):  
Performance Analysis ◽  
Image Quality ◽  
Prediction Model ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
Objective Measure ◽  
Just Noticeable Difference ◽  
Electromagnetic Spectrum ◽  
Image Datasets

Introduction/purpose: The paper presents interesting research related to the performance analysis of the picture-wise just noticeable difference (JND) prediction model and its application in the quality assessment of images with JPEG compression. Methods: The performance analysis of the JND model was conducted in an indirect way by using the publicly available results of subject-rated image datasets with the separation of images into two classes (above and below the threshold of visible differences). In the performance analysis of the JND prediction model and image quality assessment, five image datasets were used, four of which come from the visible wavelength range, and one dataset is intended for remote sensing and surveillance with images from the infrared part of the electromagnetic spectrum. Results: The pap 86 er shows that using a picture-wise JND model, subjective image quality assessment scores can be estimated with better accuracy, leading to significant performance improvements of the traditional peak signal-to-noise ratio (PSNR). The gain achieved by introducing the picture-wise JND model in the objective assessment depends on the chosen dataset and the results of the initial simple to compute PSNR measure, and it was obtained on all five datasets. The mean linear correlation coefficient (for five datasets) between subjective and PSNR objective quality estimates increased from 74% (traditional PSNR) to 90% (picture-wise JND PSNR). Conclusion: Further improvement of the JND-based objective measure can be obtained by improving the picture-wise model of JND prediction.

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

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2256
Author(s):  
Krzysztof Okarma ◽  
Piotr Lech ◽  
Vladimir V. Lukin
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
Assessment Methods ◽  
Subjective Quality ◽  
Reference Image ◽  
Exponential Weights ◽  
Full Reference ◽  
Highly Correlated

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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