scholarly journals Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danuta M. Sampson ◽  
David Alonso-Caneiro ◽  
Avenell L. Chew ◽  
Jonathan La ◽  
Danial Roshandel ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Adaptive Optics ◽  
Image Quality Assessment ◽  
Assessment Tool ◽  
Ground Truth ◽  
Disease Status ◽  
Retinal Location ◽  
Imaging Tool ◽  
Multiclass Classifier

AbstractAdaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality.

scholarly journals No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking

2019 ◽  
Vol 33 ◽  
pp. 8336-8343
Author(s):  
Jie Gu ◽  
Gaofeng Meng ◽  
Cheng Da ◽  
Shiming Xiang ◽  
Chunhong Pan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ground Truth ◽  
Superior Performance ◽  
Reference Image ◽  
Model Parameters ◽  
Ranking Problem ◽  
Ranking List ◽  
Markov Decision

Opinion-unaware no-reference image quality assessment (NR-IQA) methods have received many interests recently because they do not require images with subjective scores for training. Unfortunately, it is a challenging task, and thus far no opinion-unaware methods have shown consistently better performance than the opinion-aware ones. In this paper, we propose an effective opinion-unaware NR-IQA method based on reinforcement recursive list-wise ranking. We formulate the NR-IQA as a recursive list-wise ranking problem which aims to optimize the whole quality ordering directly. During training, the recursive ranking process can be modeled as a Markov decision process (MDP). The ranking list of images can be constructed by taking a sequence of actions, and each of them refers to selecting an image for a specific position of the ranking list. Reinforcement learning is adopted to train the model parameters, in which no ground-truth quality scores or ranking lists are necessary for learning. Experimental results demonstrate the superior performance of our approach compared with existing opinion-unaware NR-IQA methods. Furthermore, our approach can compete with the most effective opinion-aware methods. It improves the state-of-the-art by over 2% on the CSIQ benchmark and outperforms most compared opinion-aware models on TID2013.

scholarly journals Visual Scan-Path based Data-Augmentation for CNN-based 360-degree Image Quality Assessment

2021 ◽  
Vol 2021 (1) ◽  
pp. 21-26
Author(s):  
Abderrezzaq Sendjasni ◽  
Mohamed-Chaker Larabi ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Data Augmentation ◽  
Image Quality Assessment ◽  
Ground Truth ◽  
Training Data ◽  
Training Scheme ◽  
Scan Path ◽  
Scan Paths

360-degree Image quality assessment (IQA) is facing the major challenge of lack of ground-truth databases. This problem is accentuated for deep learning based approaches where the performances are as good as the available data. In this context, only two databases are used to train and validate deep learning-based IQA models. To compensate this lack, a dataaugmentation technique is investigated in this paper. We use visual scan-path to increase the learning examples from existing training data. Multiple scan-paths are predicted to account for the diversity of human observers. These scan-paths are then used to select viewports from the spherical representation. The results of the data-augmentation training scheme showed an improvement over not using it. We also try to answer the question of using the MOS obtained for the 360-degree image as the quality anchor for the whole set of extracted viewports in comparison to 2D blind quality metrics. The comparison showed the superiority of using the MOS when adopting a patch-based learning.

GPU Based Image Quality Assessment using Structural Similarity (SSIM) Index

2016 ◽  
pp. 276-282
Author(s):  
Mahesh Satish Khadtare
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Video Processing ◽  
Image Quality Assessment ◽  
Assessment Tool ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
University Of Texas ◽  
Computationally Intensive

This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.

scholarly journals Subjective annotation for a frame interpolation benchmark using artefact amplification

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Hui Men ◽  
Vlad Hosu ◽  
Hanhe Lin ◽  
Andrés Bruhn ◽  
Dietmar Saupe
Keyword(s):  
Image Quality ◽  
Optical Flow ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Visual Quality ◽  
Ground Truth ◽  
Paired Comparisons ◽  
Frame Interpolation ◽  
Full Reference

Abstract Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment crowdscouring study for the interpolated frames provided by one of the optical flow benchmarks, the Middlebury benchmark. It contains interpolated frames from 155 methods applied to each of 8 contents. For this purpose, we collected forced-choice paired comparisons between interpolated images and corresponding ground truth. To increase the sensitivity of observers when judging minute difference in paired comparisons we introduced a new method to the field of full-reference quality assessment, called artefact amplification. From the crowdsourcing data (3720 comparisons of 20 votes each) we reconstructed absolute quality scale values according to Thurstone’s model. As a result, we obtained a re-ranking of the 155 participating algorithms w.r.t. the visual quality of the interpolated frames. This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images. As a first step, we proposed such a new full-reference method, called WAE-IQA, which weights the local differences between an interpolated image and its ground truth.

A Novel Hybrid Image Quality Assessment Algorithm

2011 ◽  
Vol 4 (4) ◽  
pp. 107-108
Author(s):  
Deepa Maria Thomas ◽  
◽  
S. John Livingston
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment
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