No-reference image sharpness assessment based on discrepancy measures of structural degradation

Cai, Hao; Wang, Mingjie; Mao, Wendong; Gong, Minglun

doi:10.1016/j.jvcir.2020.102861

Cited by 9 publications

(6 citation statements)

References 69 publications

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“…In this section, we introduced learning-based methods for comparison, as shown in Table 7. Among these methods, BIQI [28], BLIINDS-II [26], BRISQUE [27], SSEQ [53], Cai's method [30] and Liu's method [31] are based on SVM, Kang's CNN [32], Yu's CNN [54], MEON [34], SGDNet [36], NSSADNN [35] and MSFF [37] are based on deep learning. NIQE [55] and SPARISH [56] belong to other learning-based methods.…”

Section: Comparison With Learning-based Methodsmentioning

confidence: 99%

“…BIQI [28], BLIINDS-II [26], BRIS-QUE [27], SSEQ [53], NIQE [55], Kang's CNN [32], MEON [34], SGDNet [36], NSSADNN [35] and MSFF [37] were proposed for general purpose, while the rest of the methods are blur-specific. Most of the codes and training models of previous methods are publicly available, but considering that the training environments and settings vary among different learning-based methods, the results of those methods were obtained directly from the corresponding reference papers [30,31,35,37,53,57,58]. We introduced PLCC and SROCC as measurements.…”

Section: Comparison With Learning-based Methodsmentioning

confidence: 99%

“…Recently, Cai et al. [30] leveraged the discrepancy measures of structural degradation in both the spatial and wavelet domains, and used a support vector regressor (SVR) to map the feature vectors into quality scores. Liu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Classical methods include BIQI [28] and DIIVINE [29]. Recently, Cai et al [30] leveraged the discrepancy measures of structural degradation in both the spatial and wavelet domains, and used a support vector regressor (SVR) to map the feature vectors into quality scores. Liu et al [31] proposed a method that integrated an orientation-aware local pattern operator which fully considered the impact of anisotropy of orientation selectivity mechanism and the gradient orientation effect on visual perception.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A no‐reference blurred colourful image quality assessment method based on dual maximum local information

Chen

2021

IET Signal Processing

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Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No-reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing-based auto-focussing and maximum local information theories, a no-reference blurred colourful IQA method based on Dual Maximum Local Information is proposed here. First, a window extraction method that combines the maximum gradient with local entropy is proposed to obtain the region of interest (ROI) for subsequent processing. Second, an improved maximum gradient method that leverages information from different channel images is presented to calculate the maximum gradient variation within the ROI for final sharpness score. Experimental results illustrated that the proposed method has better performance under various measurements compared with the state-of-the-art methods on LIVE, CSIQ, TID2008, TID2013, VCL@FER, IVC image databases.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Comparison With Learning-based Methodsmentioning

confidence: 99%

Section: Comparison With Learning-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A no‐reference blurred colourful image quality assessment method based on dual maximum local information

Chen

2021

IET Signal Processing

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“…Sun et al simulate the impact of viewing distance on blur distortion, and calculate the distribution characteristics of the local maximum gradient of multi-resolution images in the spatial domain [46]. Cai et al design an interesting procedure that reblurs the orignial blurred image [47], and the global sharpness is estimated through inter-resolution self-similarities, since the discrepancy between an image and its reblurred version indicates the extent of blur in the image. CNN has also been used for BISA tasks.…”

Section: Introductionmentioning

confidence: 99%

Salient Region Guided Blind Image Sharpness Assessment

Liu

Zhao

et al. 2021

Sensors

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Salient regions provide important cues for scene understanding to the human vision system. However, whether the detected salient regions are helpful in image blur estimation is unknown. In this study, a salient region guided blind image sharpness assessment (BISA) framework is proposed, and the effect of the detected salient regions on the BISA performance is investigated. Specifically, three salient region detection (SRD) methods and ten BISA models are jointly explored, during which the output saliency maps from SRD methods are re-organized as the input of BISA models. Consequently, the change in BISA metric values can be quantified and then directly related to the difference in BISA model inputs. Finally, experiments are conducted on three Gaussian blurring image databases, and the BISA prediction performance is evaluated. The comparison results indicate that salient region input can help achieve a close and sometimes superior performance to a BISA model over the whole image input. When using the center region input as the baseline, the detected salient regions from the saliency optimization from robust background detection (SORBD) method lead to consistently better score prediction, regardless of the BISA model. Based on the proposed hybrid framework, this study reveals that saliency detection benefits image blur estimation, while how to properly incorporate SRD methods and BISA models to improve the score prediction will be explored in our future work.

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No-reference blurred image quality assessment method based on structure of structure features

Chen

et al. 2023

Signal Processing: Image Communication

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No-reference image sharpness assessment based on discrepancy measures of structural degradation

Cited by 9 publications

References 69 publications

A no‐reference blurred colourful image quality assessment method based on dual maximum local information

A no‐reference blurred colourful image quality assessment method based on dual maximum local information

Salient Region Guided Blind Image Sharpness Assessment

No-reference blurred image quality assessment method based on structure of structure features

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