No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection

Leonardi, Marco; Napoletano, Paolo; Schettini, Raimondo; Rozza, Alessandro

doi:10.3390/s21030994

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…In contrast, Wu et al [ 19 ] boosted NIQE by more complex features to increase the prediction performance. Recently, Leonardi et al [ 21 ] utilized deep features extracted from a pre-trained convolutional neural network to construct an opinion–unaware method using the correlations through the Gramian matrix between feature maps.…”

Section: Literature Reviewmentioning

confidence: 99%

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

Varga

2022

J. Imaging

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With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings.

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Section: Literature Reviewmentioning

confidence: 99%

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

Varga

2022

J. Imaging

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“…Therefore, the development of BIQA methods is of particular importance in this field [ 13 , 14 , 15 , 16 ]. NR methods can be further classified into opinion-aware and opinion-unaware/completely blind approaches [ 17 ] depending on the access to subjective scores while creating a quality model.…”

Section: Introductionmentioning

confidence: 99%

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

Stępień

Oszust

2022

J. Imaging

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No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.

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“…The perceptual quality score is a subjective measure. It is generally defined as the mean of the individual ratings of perceived quality assigned by human subjects, and it is also called Mean Opinion Score (MOS) [3]. Several FR and RR approaches have been proposed in the literature with high performance on the task of assessing image quality [4,5].…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

2022

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The wide availability and small size of different types of sensors have allowed for the acquisition of a huge amount of data about a person’s life in real time. With these data, usually denoted as lifelog data, we can analyze and understand personal experiences and behaviors. Most of the lifelog research has explored the use of visual data. However, a considerable amount of these images or videos are affected by different types of degradation or noise due to the non-controlled acquisition process. Image Quality Assessment can plays an essential role in lifelog research to deal with these data. We present in this paper a twofold study on the topic of blind image quality assessment. On the one hand, we explore the replication of the training process of a state-of-the-art deep learning model for blind image quality assessment in the wild. On the other hand, we present evidence that blind image quality assessment is an important pre-processing step to be further explored in the context of information retrieval in lifelogging applications. We consider that our efforts have been successful in the replication of the model training process, achieving similar results of inference when compared to the original version, while acknowledging a fair number of assumptions that we had to consider. Moreover, these assumptions motivated an extensive additional analysis that led to significant insights on the influence of both batch size and loss functions when training deep learning models in this context. We include preliminary results of the replicated model on a lifelogging dataset, as a potential reproducibility aspect to be considered.

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No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection

Cited by 7 publications

References 38 publications

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

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