Light Filed Image Quality Assessment by Local and Global Features of Epipolar Plane Image

Fang, Yuming; Wei, Kangkang; Hou, Junhui; Wen, Wenying; İmamoğlu, Nevrez

doi:10.1109/bigmm.2018.8499086

Cited by 36 publications

(17 citation statements)

References 22 publications

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“…In order to demonstrate the effectiveness of our proposed NR-LFQA model, we conduct extensive experiments by using existing 2D, 3D, multi-view, and LFI quality assessment algorithms. In our experiments, we utilize ten 2D-FR metrics [17]- [20], [24]- [27], [36], three 2D-NR metrics [33]- [35], one 3D-FR metric [38], two 3D-NR metrics [40], [41], five multiview FR metrics [42]- [45], one multi-view NR metric [46], one LFI FR metric [49], and one LFI RR metric [50]. For all 2D-FR, multi-view FR, LFI FR, LF-IQM, NIQE and APT algorithms, the global predicted score of LFI is obtained by…”

Section: E Comparison With Other Objective Metricsmentioning

confidence: 99%

No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

Shi

Zhou

Chen

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.Index Terms-Light field image, quality assessment, epipolar plane image, naturalness, spatial quality, angular consistency.

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Section: E Comparison With Other Objective Metricsmentioning

confidence: 99%

No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

Shi

Zhou

Chen

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…For the form of uniting multiple representations, Luo et al ( 2019 ) used the global entropy and uniform local binary pattern features of a lenslet image to evaluate the angular consistency, and adopted the information entropy of SAIs to measure spatial quality. Fang et al ( 2018 ) calculated the change in visual quality by combining the gradient amplitude of SAIs and EPIs.…”

Section: Introductionmentioning

confidence: 99%

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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“…In the literature, several LFI quality assessment models have been proposed. Fang et al [38] proposed a FR LFI quality assessment method that measures the gradient magnitude similarity of reference and distorted epipolar plane images. Huang et al [39] also proposed a FR LFI quality assessment algorithm, which is based on dense distortion curve analysis and scene information statistics.…”

Section: Introductionmentioning

confidence: 99%

“…The light field image quality assessment metric (LF-IQM) [40] is a RR LFI quality assessment metric that assumes the depth map quality is closely related to the LFI overall quality and measures the structural similarity between original and distorted depth maps to predict the perceived LFI quality. However, Fang [38] and LF-IQM [40] ignore the texture information of SAI, which result in the insufficient measurement of the LFI spatial quality. Furthermore, the performance of the LF-IQM is significantly affected by the adopted depth estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Although luminance is considered as a dominant factor for understanding the human visual perception [43], luminance-based IQA methods may be sub-optimal because they underestimate visual interference caused by color distortion, especially the significant differences in the colors of different SAIs. In addition, existing methods neglect the impact of angular consistency in diverse orientations [40], [41] or only consider horizontal angular consistency [38], [39]. Since the LFI is an image array, the relationship between each SAI and the adjacent SAI can reflect the LFI angular consistency.…”

Section: Introductionmentioning

confidence: 99%

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Tensor Oriented No-Reference Light Field Image Quality Assessment

Zhou

Shi

Chen

et al. 2020

IEEE Trans. on Image Process.

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Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media. However, due to the inherent high dimensional characteristics of LFI, the LFI quality assessment turns into a multi-dimensional problem that requires consideration of the quality degradation in both spatial and angular dimensions. Therefore, we propose a novel Tensor oriented No-reference Light Field image Quality evaluator (Tensor-NLFQ) based on tensor theory. Specifically, since the LFI is regarded as a low-rank 4D tensor, the principle components of four oriented sub-aperture view stacks are obtained via Tucker decomposition. Then, the Principal Component Spatial Characteristic (PCSC) is designed to measure the spatialdimensional quality of LFI considering its global naturalness and local frequency properties. Finally, the Tensor Angular Variation Index (TAVI) is proposed to measure angular consistency quality by analyzing the structural similarity distribution between the first principal component and each view in the view stack. Extensive experimental results on four publicly available LFI quality databases demonstrate that the proposed Tensor-NLFQ model outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.Index Terms-Light field, image quality assessment, objective model, tensor theory, angular consistency.

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Light Filed Image Quality Assessment by Local and Global Features of Epipolar Plane Image

Cited by 36 publications

References 22 publications

No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

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

Tensor Oriented No-Reference Light Field Image Quality Assessment

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