No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image

Luo, Ziyuan; Zhou, Wei; Shi, Likun; Chen, Zhibo

doi:10.1109/pcs48520.2019.8954551

Cited by 19 publications

(11 citation statements)

References 40 publications

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“…BELIF [78], QMLI [79], LFQA-DSA [13], LFQA [10], Cui et al [80], ALAS-DADS [14], Alamgeer and Farias [81]. The results, reported in Table V, show that the proposed metric outperforms state-of-the-art approaches.…”

Section: Performance Comparison With State-of-the-art Quality Metricsmentioning

confidence: 83%

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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Section: Performance Comparison With State-of-the-art Quality Metricsmentioning

confidence: 83%

No-Reference Light Field Image Quality Assessment Exploiting Saliency

Lamichhane

Neri

Battisti

et al. 2023

IEEE Trans. on Broadcast.

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show abstract

“…As performance evaluation methods, we used only the Spearman's Rank-Order Correlation Coefficient (SROCC) and the Pearson's Linear Correlation Coefficient (PLCC) for simplicity. We compared the proposed NR LF-IQA method with the following state-of-art LF-IQA methods: MDFM (Tian et al, 2018), LFIQM (Paudyal et al, 2019), Fang et al (Fang et al, 2018), SDFM (Tian et al, 2020b), Meng et al (Meng et al, 2020), LGF-LFC (Tian et al, 2020a), NR-LFQA (Shi et al, 2019a), LF-QMLI (Luo et al, 2019), Jiang et al (Jiang et al, 2018), BELIF Shi et al, 2020). We also compared the proposed method with the following 2D image/video quality metrics: PSNR-YUV (Sze et al, 2014), IW-PSNR (Wang and Li, 2011), FI-PSNR (Lin and Wu, 2014), MW-PSNR (Sandić-Stanković et al, 2016), SSIM (Wang et al, 2004), IW-SSIM (Wang and Li, 2011), UQI (Zhou and Bovik, 2002), VIF , MJ3DFR (Chen et al, 2013), GMSD (Xue et al, 2014), NICE (Rouse and Hemami, 2009) and STMAD (Vu et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…The NR LFQA method proposed by Shi et al (Shi et al, 2019a) predicts quality using EPI information and natural statistics. The NR LF-IQA method proposed by Luo et al (Luo et al, 2019) employs the spatial information from SAIs and the angular information from the micro-lens images. Jiang et al (Jiang et al, 2018) proposed a FR LF-IQA method that uses the entropy information and the gradient magnitude features to extract spacial features from the SAIs.…”

Section: Introductionmentioning

confidence: 99%

Blind visual quality assessment of light field images based on distortion maps

Alamgeer

Farias

2022

Front. Signal Process.

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Light Field (LF) cameras capture spatial and angular information of a scene, generating a high-dimensional data that brings several challenges to compression, transmission, and reconstruction algorithms. One research area that has been attracting a lot of attention is the design of Light Field images quality assessment (LF-IQA) methods. In this paper, we propose a No-Reference (NR) LF-IQA method that is based on reference-free distortion maps. With this goal, we first generate a synthetically distorted dataset of 2D images. Then, we compute SSIM distortion maps of these images and use these maps as ground error maps. We train a GAN architecture using these SSIM distortion maps as quality labels. This trained model is used to generate reference-free distortion maps of sub-aperture images of LF contents. Finally, the quality prediction is obtained performing the following steps: 1) perform a non-linear dimensionality reduction with a isometric mapping of the generated distortion maps to obtain the LFI feature vectors and 2) perform a regression using a Random Forest Regressor (RFR) algorithm to obtain the LF quality estimates. Results show that the proposed method is robust and accurate, outperforming several state-of-the-art LF-IQA methods.

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“…Thus, a suitable subjective method can support and strengthen the results of objective methods. Currently, the objective quality assessment for LF images is an active area of research [12][13][14]. There is a focus on the design of LF-IQA metrics that consider the LF characteristics (i.e., changing color information with view angle and depth information) through the analysis and extraction of LF features.…”

Section: 𝑃 = 𝐿(𝑢 𝑣 𝑠 𝑡)mentioning

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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No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image

Cited by 19 publications

References 40 publications

No-Reference Light Field Image Quality Assessment Exploiting Saliency

No-Reference Light Field Image Quality Assessment Exploiting Saliency

Blind visual quality assessment of light field images based on distortion maps

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

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