Image quality assessment using BSIF, CLBP, LCP, and LPQ operators

Freitas, Pedro Garcia; Eira, Luísa Peixoto da; Santos, Samuel Soares

doi:10.1016/j.tcs.2019.10.038

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…For a detailed calculation of the coordinates of the neighboring points in OCPP, please refer to [50,54]. More extended LBP descriptors used in BIQA, including local configuration patterns (LCP) [55], local phase quantization (LPQ) [56], etc., can be found in [54,57].…”

Section: Texture Featuresmentioning

confidence: 99%

Progress in Blind Image Quality Assessment: A Brief Review

2023

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As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem.

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Section: Texture Featuresmentioning

confidence: 99%

Progress in Blind Image Quality Assessment: A Brief Review

2023

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“…Recently, many image quality methods have been proposed for perceptual quality assessment of natural images [29][30][31][32][33][34][35]. Some of these models use statistics of completed local binary patterns (CLBP) as a part of their feature vectors.…”

Section: Related Workmentioning

confidence: 99%

“…In [33], joint statistics of local binary patterns (LBP) and CLBP patterns produced quality-aware features, and a regression function was trained to map the feature space to the perceived quality scores. In [32], features based on several local image descriptors such as CLBP, local configuration patterns (LCP), and local phase quantization (LPQ) were extracted, and then a support vector regressor was used to predict the quality scores. These models are trained to predict the perceptual quality of natural images.…”

Section: Related Workmentioning

confidence: 99%

Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

Jenadeleh

Pedersen

Saupe

2020

Sensors

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Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.

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