A No-Reference Image Quality Assessment Metric by Multiple Characteristics of Light Field Images

Shan, Liang; An, Ping; Meng, Chunli; Yang, Chao; Shen, Liquan

doi:10.1109/access.2019.2940093

Cited by 47 publications

(13 citation statements)

References 36 publications

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“…Resource identification initiative. To verify the performance of the proposed method, experiments were conducted on three subjective quality assessment databases of LF images, including the database of traditional distortion types: SHU (Shan et al, 2019 ), video compression, and LF compression types: VALID-10bit (Viola and Ebrahimi, 2018 ), and LF reconstruction types: NBU-LF1.0 (Huang et al, 2019b ). The detailed information of these databases is listed in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, some studies have attempted to combine depth features with the features from SAIs to achieve better prediction results. Shan et al ( 2019 ) combined the ordinary 2D features of SAIs and sparse gradient dictionary of LF depth map. Tian et al ( 2020 ) performed radial symmetric transformation on the luminance components of all dense viewpoints to extract symmetric features and used depth maps to measure the structural consistency between viewpoints, which explored the way humans perceive structures and geometries.…”

Section: Introductionmentioning

confidence: 99%

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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: 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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“…These works consider the LF characteristics by exploiting the depth, inter-view and angular information, e.g., symmetry feature extraction, depth feature extraction, global spatial quality, local spatial quality and angular quality estimation. Likewise, in Shan et al [29], the author presents an NR metric that exploits 2D and 3D characteristics (i.e., the brightness, hue, saturation and texture features) of the LF with support vector regression and the depth information to obtain the final prediction score. In addition, an RR metric has been proposed by Viola [25], which takes the LF depth map as a reference.…”

Section: Objective Quality Metrics For Lf Imagesmentioning

confidence: 99%

Objective Quality Assessment Metrics for Light Field Image Based on Textural Features

et al. 2022

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Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account the image content and human visual system such as SSIM and IW-SSIM. However, most of these metrics are designed for images and video with natural content. Additionally, other models based on the LF characteristics (e.g., depth information, angle information) trade high performance for high computational complexity, along with them possessing difficulties of implementation for LF applications due to the immense data requirements of LF images. Hence, this paper presents a novel content-adaptive LF-IQA metric to improve the conventional LF-IQA performance that is also low in computational complexity. The experimental results clearly show improved performance compared to conventional objective IQA metrics, and we also identify metrics that are well-suited for LF image assessment. In addition, we present a comprehensive content-based feature analysis to determine the most appropriate feature that influences human visual perception among the widely used conventional objective IQA metrics. Finally, a rich LF dataset is selected from the EPFL dataset, allowing for the study of light field quality by qualitative factors such as depth (wide and narrow), focus (background or foreground) and complexity (simple and complex).

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“…Moreover, there have been several attempts to develop specific LF objective quality metrics for LF images. Tamboli et al [7] proposed a full-reference image quality evaluation algorithm where both spatial and angular quality were Shan et al [8] proposed a no-reference LF image quality metric. The proposed model is composed of a color component, texture component, and a depth component.…”

Section: Related Workmentioning

confidence: 99%

“…Authors have used Wavelet decomposition to predict the spatial quality component. Optical flow has been used to predict the angular quality component.Shan et al[8] proposed a no-reference LF image quality metric. The proposed model is composed of a color component, texture component, and a depth component.…”

mentioning

confidence: 99%

No-Reference Quality Evaluation of Light Field Content Based on Structural Representation of The Epipolar Plane Image

Ak¹,

Ling²,

Callet³

2020

2020 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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As an emerging technology, Light Field (LF) has gained everincreasing importance in the domain. In order to provide guidance for the development of perceptually accurate Light Field (LF) processing tools and supervise the entire streaming system, robust perceptual quality assessment metrics are required. Especially, No-Reference (NR) metrics are preferable to compare LF with different angular resolutions. Some metrics have been developed by extending commonly used 2D image quality metrics to the 4D LF domain with angular consistency terms. Nonetheless, although these models consistently show slightly improved performance, most of them are limited in evaluating the quality of LF using the sub-aperture views with additional terms on the angular domain. There is an evident lack of reliable quality metrics that are tailored to LF content. To remedy this lack, we propose a NR quality metric for LF contents based on representing EPI with structural descriptors, including the Histogram of Gradients and the Convolutional Sparse Coding based descriptors. The primary motivation resides in our observations that (1) LF related distortions on the angular domain are highly noticeable on the Epipolar Plane Image representations (EPI); (2) most of the distortions in EPI are structurerelated. Extensive experiments on the MPI-LFA [1] LF image quality dataset demonstrate that our method provides competitive performance with the state-of-the-art NR image quality metrics.

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A No-Reference Image Quality Assessment Metric by Multiple Characteristics of Light Field Images

Cited by 47 publications

References 36 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

Objective Quality Assessment Metrics for Light Field Image Based on Textural Features

No-Reference Quality Evaluation of Light Field Content Based on Structural Representation of The Epipolar Plane Image

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