Blind image quality assessment in the contourlet domain

Li, Chaofeng; Guan, Tuxin; Zheng, Yuhui; Zhong, Xiaochun; Wu, Xiaojun; Bovik, Alan C.

doi:10.1016/j.image.2020.116064

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…Natural scene statistics (NSS) has the statistical characteristics of HSV perception: high sensitivity to structural, perceptual masking, orientation, multiscale, clearly visible distortions, and Natural scene statistics (NSS) extracts, acquires a low level of statistical regularity, measures the destruction of the naturalness introduced by distortions [7], describes it by using parametric distributions of different transformed domains, spatial domain [7] [8], discrete cosine transformed domain [ 9], contourlet domains [10] and hybrid domains [11]. Typically, the extracted features are mapped to quality scores using the Support Vector Regressor (SVR).…”

Section: Multi-dimensional Reference-free Image Quality Evaluation In...mentioning

confidence: 99%

An image quality evaluation assessment based on natural scene statistics

Chen,

Wang

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Aiming at the mosaic phenomenon in images, the existing single-feature evaluation indexes of non-referential image quality do not conform to the subjective evaluation of the human eye, and a single-dimensional evaluation standard of edge slope consistency is proposed. For the super-resolution reconstruction image quality evaluation research, the proposed multi-dimensional non-referential image quality evaluation index, can simulate the subjective evaluation of the calculation of image quality real-time scoring, and distinguish between different super-resolution reconstruction algorithm results. Design and analysis of the experimental results, the unidimensional and multi-dimensional image quality evaluation standard proposed in this paper is more in line with the subjective evaluation of the human eye, and at the same time, combined with the database LIVE and QADS compared with the other image evaluation index test, which illustrates the superiority and versatility of the multi-dimensional image quality index.

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Section: Multi-dimensional Reference-free Image Quality Evaluation In...mentioning

confidence: 99%

An image quality evaluation assessment based on natural scene statistics

Chen,

Wang

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…The contourlet transform (CT) [19], [20] performs better than the classical wavelet in many image processing tasks due to the characteristics of shift sensitivity and directionality. However, the lack of translation invariance leads to pseudo-Gibbs phenomena through the contourlet transform.…”

Section: Complex Contourlet Transformmentioning

confidence: 99%

Visibility and Distortion Measurement for No-Reference Dehazed Image Quality Assessment via Complex Contourlet Transform

Guan

et al. 2023

IEEE Trans. Multimedia

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Recently, most dehazed image quality assessment (DQA) methods have focused on estimating remaining haze and omitting distortion impact from the side effect of dehazing algorithms, which leads to their limited performance. Addressing this problem, we propose a method for learning both visibility and distortion-aware features no-reference (NR) dehazed image quality assessment (VDA-DQA). Visibility-aware features are exploited to characterize clarity optimization after dehazing, including the brightness-, contrast-, and sharpness-aware features extracted by the complex contourlet transform (CCT). Then, distortion-aware features are employed to measure the distortion artifacts of images, including the normalized histogram of the local binary pattern (LBP) from the reconstructed dehazed image and the statistics of the CCT subbands corresponding to the chroma and saturation map. Finally, all the above features are mapped into quality scores by support vector regression (SVR). Extensive experimental results on six public DQA datasets verify the superiority of the proposed VDA-DQA method in terms of consistency with subjective visual perception and outperform state-of-the-art methods. The source code of VDA-DQA is available at https://github.com/li181119/VDA-DQA.

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“…Image quality has a profound impact on subsequent work [13]. Different from other environments, the underwater images have many problems such as noise interference, blurred texture features, low contrast and colour distortion.…”

Section: Data Processingmentioning

confidence: 99%

Underwater Object Detection Based on Improved Transformer and Attentional Supervised Fusion

Guan

et al. 2023

ITC

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Underwater object detection is one of the important technologies for improving the efficiency of underwater inspection, but the existing methods still suffer from the problems of missed detection and insufficient target localization capability of targets. To address these problems, an improved Transformer and multi-scale attentional supervised feature fusion-based underwater object detection method is proposed. In our method, the underwater objects are preprocessed by prior knowledge first. Then, a new coordinate decomposition window-based (CDW) Transformer block is proposed to extract spatial location information more accurately, and scaling factors are introduced to reduce the intermediate computation. Finally, an attentional supervised fusion (ASF) method is proposed to strengthen the link between feature extraction and feature fusion, and further improve the detected performance by using compound attention weights. The cascade detection head is improved, where the information flow is reversed to enhance the prediction of coordinates. The average accuracy of the proposed method on the URPC and DUO datasets is 3.7% and 3.8% higher than that of the baseline network through the cross-test, and outperforms the state-of-the-art methods. This study can provide a reference for engineering applications such as automated marine operations and biodetected fishing techniques.

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Blind image quality assessment in the contourlet domain

Cited by 17 publications

References 37 publications

An image quality evaluation assessment based on natural scene statistics

An image quality evaluation assessment based on natural scene statistics

Visibility and Distortion Measurement for No-Reference Dehazed Image Quality Assessment via Complex Contourlet Transform

Underwater Object Detection Based on Improved Transformer and Attentional Supervised Fusion

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