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

Meng, Chunli; An, Ping; Yang, Chao; Chen, Yilei

doi:10.3389/fncom.2021.768021

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…The FR/RR LFIQA metrics [38], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53] evaluate the perceptual quality of distorted LFIs by using the full/partial reference information. For example, Fang et al [43] propose a FR LFIQA metric by calculating the similarity between the gradient magnitudes of reference and distorted SAIs and EPIs.…”

Section: B Quality Assessment Of Lfismentioning

confidence: 99%

Blind Perceptual Quality Assessment of LFI Based on Angular-Spatial Effect Modeling

Zhang,

Tian,

Zhang

et al. 2024

IEEE Trans. on Broadcast.

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By recording scenes from multiple viewpoints, Light Field Image (LFI) encompasses both angular and spatial information, thereby offering users a more immersive experience. Since LFIs may be distorted at various stages from acquisition to visualization, Light Field Image Quality Assessment (LFIQA) is of vitally important to monitor the potential impairments of LFI quality. However, existing objective LFIQA metrics fail to establish a reasonable correlation between spatial and angular information in LFIs, especially ignoring the imbalance problem of large spatial variations and subtle angular variations, which results in unsatisfactory quality evaluation performance. To alleviate this imbalance, in this paper, we propose a novel Blind LFIQA metric based on Angular-Spatial Effect Modeling, abbreviated as ASEM-BLiF. Specifically, the proposed metric consists of two branches. In the principal branch, we first present an Angular Effect Modeling (AEM) module to capture the angular information independently of spatial information. Based on AEM, we further design an Angular-Spatial Quality Learning (ASQL) module to model the local angular-spatial effect and establish the global relationship between different local regions for quality assessment via Transformer. In the auxiliary branch, a Discriminative Region Selection (DRS) module is proposed for auxiliary learning to improve the learning efficiency and prediction accuracy from a local perspective. Moreover, we present a Dynamic Weighting Loss (DWLoss) to achieve an optimal balance between principal and auxiliary learning throughout training. To demonstrate the effectiveness of the proposed metric, extensive experiments are conducted on five publicly available LFIQA databases with a variety of metrics.The experimental results show that compared to our previous work DeeBLiF, the current state-of-the-art LFIQA metric, our proposed ASEM-BLiF metric achieves 5.67%, 7.75%, 5.96%, 4.44%, and 0.33% SROCC performance improvements in quality assessment on the Win5-LID, NBU-LF1.0, LFDD, VALID10bit, and SHU databases, respectively. The code will be publicly available.

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Section: B Quality Assessment Of Lfismentioning

confidence: 99%

Blind Perceptual Quality Assessment of LFI Based on Angular-Spatial Effect Modeling

Zhang,

Tian,

Zhang

et al. 2024

IEEE Trans. on Broadcast.

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show abstract

“…As mentioned before, the existing objective LF-IQA metrics can be classified into FR, RR, and NR categories. The FR LF-IQA metrics [6], [7], [33], [34], [35], [36], [37], [38], [39], [40] assess the quality of the distorted LFI when the originally pristine information is available. For example, KRIQE [7] exploits the gradient magnitude and phase congruency of the key reference and distorted RIs for LFI quality evaluation.…”

Section: Related Workmentioning

confidence: 99%

PVBLiF: A Pseudo Video-Based Blind Quality Assessment Metric for Light Field Image

Zhang

Tian

Zou

et al. 2023

IEEE J. Sel. Top. Signal Process.

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Going beyond traditional 2D imaging is not only an emerging trend of imaging technology, but also the key to a more immersive user experience. Light Field Image (LFI) is a typical high-dimensional imaging format, and the quality evaluation of which is very challenging but necessary. In this paper, we propose a novel Pseudo Video-based Blind quality assessment metric for Light Field image (PVBLiF). In contrast to most previous Light Field Image Quality Assessment (LF-IQA) metrics, in which different types of 2D representations derived from LFI are used for quality assessment indirectly, our metric exploits a more intuitive 3D representation, named Pseudo Video Block Sequence (PVBS), to evaluate the perceptual quality of LFI. For this purpose, we first divide the LFI into a massive number of non-overlapping PVBSs, which simultaneously contain spatial and angular information of LFI. Then, we propose a novel network (named PVBSNet) based on Convolutional Neural Networks (CNNs) to extract the spatio-angular features of PVBS and further evaluate the PVBS quality. The proposed PVBSNet consists of four stages: multi-information division, intra-feature extraction, cross-feature fusion, and quality regression. Finally, a Saliency-and Variance-guided Pooling (SVPooling) method is presented to integrate all the PVBS quality into the overall quality of LFI. The proposed PVBLiF metric has been extensively evaluated on three widely-used LFI datasets: Win5-LID, NBU-LF1.0, and SHU. Experimental results demonstrate that our proposed PVBLiF metric outperforms state-of-the-art metrics and is capable of highly approximating the performance of human observers. The source code of PVBLiF is publicly available at https://github.com/ZhengyuZhang96/PVBLiF.

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Splitting the backbone: A novel hierarchical method for assessing light field image quality

Guo,

Wang,

2024

Optics and Lasers in Engineering

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

Cited by 4 publications

References 52 publications

Blind Perceptual Quality Assessment of LFI Based on Angular-Spatial Effect Modeling

Blind Perceptual Quality Assessment of LFI Based on Angular-Spatial Effect Modeling

PVBLiF: A Pseudo Video-Based Blind Quality Assessment Metric for Light Field Image

Splitting the backbone: A novel hierarchical method for assessing light field image quality

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