Comprehensive image quality assessment via predicting the distribution of opinion score

Liu, An-An; Wang, Jingting; Liu, Jing; Su, Yuting

doi:10.1007/s11042-018-6985-2

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…It can be seen that the sparse representation algorithm has an ideal effect on the ERT image quality score. Finally, Pearson's linear correlation coefficient (PLCC), Spearman's rank ordered correation coefficient (SROCC), and root mean square error (RMSE) [12] are used as objective indicators to measure the performance of the image quality model. The PLCC coefficient and RMSE are mainly used to evaluate the accuracy of the predicted value, and the SROCC coefficient is mainly used to evaluate the correlation between the predicted value and the subjective value.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

ERT Image Evaluation Based on Sparse Representation Algorithm

Song¹,

Wang²,

Li³

2020

dtetr

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Most of the existing image quality evaluation is to extract image features, and then use support vector regression, sparse representation and other algorithms to evaluate the image quality. In this paper, an adaptive sub-dictionary image evaluation algorithm based on sparse representation is used. By extracting relevant image features such as gradient features and color features, a complete dictionary is first constructed using the features, and then a sparse representation method based on the sub-dictionary is used to obtain the sparse corresponding to the ERT image. Coefficient, the final image quality score is obtained by the corresponding formula.

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Section: Analysis Of Resultsmentioning

confidence: 99%

ERT Image Evaluation Based on Sparse Representation Algorithm

Song¹,

Wang²,

Li³

2020

dtetr

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“…An approach currently popular with BIQA methods is predicting a quality probability distribution, which can be interpreted using different statistical measures to derive the final quality value. Liu et al [28] first used a CNN to extract a latent feature vector from an input image. The authors then used a separate model, called the Label Distribution Support Vector Regressor (LDSVR).…”

Section: Explaining Model Predictionsmentioning

confidence: 99%

Probabilistic Fingermark Quality Assessment with Quality Region Localisation

Oblak

Haraksim

Beslay

et al. 2023

Sensors

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The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.

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“…For example, the researchers predicted the aesthetic quality score histogram of each image in [21]. In [22], the authors predicted the image quality score histogram by assuming that the quality scores of each image follow the Gaussian distribution. In [23], the authors proposed to use a deep drift-diffusion model to predict the image aesthetic score histogram.…”

Section: Introductionmentioning

confidence: 99%

Parameterized Image Quality Score Distribution Prediction

Gao¹,

Min²,

Zhu³

et al. 2022

Preprint

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Comprehensive image quality assessment via predicting the distribution of opinion score

Cited by 8 publications

References 32 publications

ERT Image Evaluation Based on Sparse Representation Algorithm

ERT Image Evaluation Based on Sparse Representation Algorithm

Probabilistic Fingermark Quality Assessment with Quality Region Localisation

Parameterized Image Quality Score Distribution Prediction

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