Blind light field image quality assessment by analyzing angular-spatial characteristics

Cui, Yueli; Yu, Mei; Jiang, Zhidi; Peng, Zongju; Chen, Fen

doi:10.1016/j.dsp.2021.103138

Cited by 34 publications

(8 citation statements)

References 56 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.

View full text Add to dashboard Cite

show abstract

“…The geographic information spatial features in the process of drone aerial surveying and mapping will be presented in a coordinate system [8][9] , with world coordinates and pixel coordinates as references, where 1 is the homogeneous form of world coordinates and 2 is the homogeneous form of pixel coordinates. Describing the position of an object in a planar similarity relationship, it is represented in planar coordinates as:…”

Section: Geographic Information Spatial Feature Extractionmentioning

confidence: 99%

Geographic information spatial feature extraction based on drone aerial surveying and mapping technology

Lai,

2024

International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024)

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“…This metric measures the structural similarity of depth maps between the original and distorted LF to predict the quality of the distorted LF. Regarding the state-of-the-art LF quality assessment methods based on quality scores, there are also several recent studies [30][31][32][33][34] that present the overview of LF-IQA with NR or blind LF approaches based on LF characteristics (i.e., spatial-angular information) and also provide a flexible coding scheme based on block Krylov subspace approximation for LF displays.…”

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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Blind light field image quality assessment by analyzing angular-spatial characteristics

Cited by 34 publications

References 56 publications

No-Reference Light Field Image Quality Assessment Exploiting Saliency

No-Reference Light Field Image Quality Assessment Exploiting Saliency

Geographic information spatial feature extraction based on drone aerial surveying and mapping technology

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

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