Objective Quality Assessment for Light Field Based on Refocus Characteristic

Meng, Chunli; An, Ping; Yang, Chao

doi:10.1007/978-3-030-34113-8_17

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…For the FR LF-IQA, the concept of optimal parallax range of human eyes is introduced into the focus stack to calculate the quality of LF images. Meng et al ( 2019 ) used some camera parameters provided by the EPFL database (Honauer et al, 2016 ) when calculating the optimal parallax range, while some databases do not have these parameters. Therefore, in combination with the experiments of refocusing factors in section 4.7, we set the focusing range of Meng et al ( 2020 ) as [−3, 3] over all databases for the sake of fairness.…”

Section: Resultsmentioning

confidence: 99%

“…The focus stack is constructed by stacking the refocused images from the perspective of depth, which reflects both the texture and depth information of LF images. Meng et al ( 2019 ) compared different objective metrics under SAIs and the focus stack, which verified the superiority of the refocus characteristic of LF images. Meng et al ( 2019 ) utilized the LF angular-spatial and human visual characteristics and verified the effectiveness of the assumed optimal parallax range.…”

Section: Introductionmentioning

confidence: 90%

“…Meng et al ( 2019 ) compared different objective metrics under SAIs and the focus stack, which verified the superiority of the refocus characteristic of LF images. Meng et al ( 2019 ) utilized the LF angular-spatial and human visual characteristics and verified the effectiveness of the assumed optimal parallax range. Meng et al ( 2021 ) built a key refocused image extraction framework based on the maximal spatial information contrast and the minimal angular information variation to reduce the redundancy of quality evaluation in the focus stack.…”

Section: Introductionmentioning

confidence: 90%

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Image Quality Evaluation of Light Field Image Based on Macro-Pixels and Focus Stack

Meng

Yang

et al. 2022

Front. Comput. Neurosci.

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Due to the complex angular-spatial structure, light field (LF) image processing faces more opportunities and challenges than ordinary image processing. The angular-spatial structure loss of LF images can be reflected from their various representations. The angular and spatial information penetrate each other, so it is necessary to extract appropriate features to analyze the angular-spatial structure loss of distorted LF images. In this paper, a LF image quality evaluation model, namely MPFS, is proposed based on the prediction of global angular-spatial distortion of macro-pixels and the evaluation of local angular-spatial quality of the focus stack. Specifically, the angular distortion of the LF image is first evaluated through the luminance and chrominance of macro-pixels. Then, we use the saliency of spatial texture structure to pool an array of predicted values of angular distortion to obtain the predicted value of global distortion. Secondly, the local angular-spatial quality of the LF image is analyzed through the principal components of the focus stack. The focalizing structure damage caused by the angular-spatial distortion is calculated using the features of corner and texture structures. Finally, the global and local angular-spatial quality evaluation models are combined to realize the evaluation of the overall quality of the LF image. Extensive comparative experiments show that the proposed method has high efficiency and precision.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 90%

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Image Quality Evaluation of Light Field Image Based on Macro-Pixels and Focus Stack

Meng

Yang

et al. 2022

Front. Comput. Neurosci.

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“…These methods usually average the scores obtained by applying the 2D full-reference metrics to single views or sub-aperture images of the LFI. Recently, specifically designed LFI fullreference quality metrics have been proposed [5], [6], [7]. A Log-Gabor-based model is proposed in [5] where the saliency features are extracted from the reference and distorted LFIs, employing the multi-scale and single-scale Gabor wavelet.…”

Section: A Full and Reduced-reference Quality Metricsmentioning

confidence: 99%

“…On the other hand, objective metrics are complex to design and, often, only partially matching the subjective judgment. In full-reference [5], [6], [7] or reduced-reference [8], [9] metrics the full or part of the spatial and angular features of the original LFI are generally used. However the availability of this information may be difficult, especially in a broadcasting scenario, thus motivating the development of no-reference metrics.…”

mentioning

confidence: 99%

No-Reference Light Field Image Quality Assessment Exploiting Saliency

Lamichhane

Neri

Battisti

et al. 2023

IEEE Trans. on Broadcast.

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In the near future, the broadcasting scenario will be characterized by immersive content. One of the systems for capturing the 3D content of a scene is the Light Field imaging. The huge amount of data and the specific transmission scenario impose strong constraints on services and applications. Among others, the evaluation of the quality of the received media cannot rely on the original signal but should be based only on the received data. In this direction, we propose a no-reference quality metric for light field images which is based on spatial and angular characteristics. In more details, the estimated saliency and cyclopean maps of light field images are exploited to extract the spatial features. The angular consistency features are, instead, measured with the use of the Global Luminance Distribution knowledge and the Weighted Local Binary Patterns operator on Epipolar Plane Images. The effectiveness of the proposed metric is assessed by comparing its performance with state-of-the-art quality metrics using 4 datasets: SMART, Win5-LID, VALID 10-bit, and VALID 8-bit. Furthermore, the performance is analyzed in crossdatasets, with different distortions, and for different saliency maps. The achieved results show that the performance of the proposed model outperforms state-of-the-art approaches and perform well for different distortion types and with various saliency models.

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Objective Quality Assessment of Lenslet Light Field Image Based on Focus Stack

Meng

Yang

et al. 2022

IEEE Trans. Multimedia

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Objective Quality Assessment for Light Field Based on Refocus Characteristic

Cited by 4 publications

References 22 publications

Image Quality Evaluation of Light Field Image Based on Macro-Pixels and Focus Stack

Image Quality Evaluation of Light Field Image Based on Macro-Pixels and Focus Stack

No-Reference Light Field Image Quality Assessment Exploiting Saliency

Objective Quality Assessment of Lenslet Light Field Image Based on Focus Stack

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