Image Quality Classification Task
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An Image Quality Classification Task is an image classification task whose output is an image quality category.
- AKA: Image Qualty Assessment (IQA).
- Context:
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- Example(s):
- Counter-Example(s):
- See: Image Description Generation, Image Quality Measure.
References
2022
- (Maier et al., 2022) ⇒ Kate Maier, Luiz Zaniolo, and Oge Marques. (2022). “Image Quality Issues in Teledermatology: A Comparative Analysis of Artificial Intelligence Solutions.” Journal of the American Academy of Dermatology, 87(1).
- ABSTRACT: ... Many models are built using high-quality professional images from publicly available datasets, introducing biases to the data. However, with the rise in teledermatology, patient-recorded images are often taken in poor, unnatural lighting, increasing the likelihood of inadequate contrast, color, or exposure, which presents a challenge to those models. ...
2017
- (Yu et al., 2017) ⇒ FengLi Yu, Jing Sun, Annan Li, Jun Cheng, Cheng Wan, and Jiang Liu. (2017). “Image Quality Classification for DR Screening Using Deep Learning.” In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 664-667 . IEEE,
- ABSTRACT: The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.
2016
- (Mahapatra et al., 2016) ⇒ Dwarikanath Mahapatra, Pallab K. Roy, Suman Sedai, and Rahil Garnavi. (2016). “Retinal Image Quality Classification Using Saliency Maps and CNNs.” In: International Workshop on Machine Learning in Medical Imaging, pp. 172-179 . Springer, Cham,
- ABSTRACT: Retinal image quality assessment (IQA) algorithms use different hand crafted features without considering the important role of the human visual system (HVS). We solve the IQA problem using the principles behind the working of the HVS. Unsupervised information from local saliency maps and supervised information from trained convolutional neural networks (CNNs) are combined to make a final decision on image quality. A novel algorithm is proposed that calculates saliency values for every image pixel at multiple scales to capture global and local image information. This extracts generalized image information in an unsupervised manner while CNNs provide a principled approach to feature learning without the need to define hand-crafted features. The individual classification decisions are fused by weighting them according to their confidence scores. Experimental results on real datasets demonstrate the superior performance of our proposed algorithm over competing methods.
2012
- (Pedersen & Hardeberg, 2012) ⇒ Marius Pedersen, and Jon Yngve Hardeberg. (2012). “Full-reference Image Quality Metrics: Classification and Evaluation.” Foundations and Trends ® in Computer Graphics and Vision, 7(1). https://dx.doi.org/10.1561/0600000037
- ABSTRACT: The wide variety of distortions that images are subject to during acquisition, processing, storage, and reproduction can degrade their perceived quality. Since subjective evaluation is time-consuming, expensive, and resource-intensive, objective methods of evaluation have been proposed. One type of these methods, image quality (IQ) metrics, have become very popular and new metrics are proposed continuously. This paper aims to give a survey of one class of metrics, full-reference IQ metrics. First, these IQ metrics were classified into different groups. Second, further IQ metrics from each group were selected and evaluated against six state-of-the-art IQ databases.