Objective Quality Assessment of Lenslet Light Field Image Based on Focus Stack

Meng, Chunli; An, Ping; Yang, Chao; Shen, Liquan; Wang, Bin

doi:10.1109/tmm.2021.3096071

Cited by 34 publications

(17 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth mentioning that quality metrics developed specifically for light fields exist (see e.g. [68], [69], [70], [71], [72]). Some of them, however, were developed and tested based on quality scores of subjects visualising content in commercial displays not designed for the visualisation of light fields.…”

Section: B Objective Metricsmentioning

confidence: 99%

Datasets for the Quality Assessment of Light Field Imaging: Comparison and Future Directions

Shafiee

Martini

2023

IEEE Access

View full text Add to dashboard Cite

With the increasing research focus on light field imaging in recent years, it has become essential for researchers in this field to either benefit from access to equipped laboratories with light field acquisition devices and displays or to have access to publicly available light field imaging datasets. Some datasets are indeed available, each with a different nature. For instance, some contain real world images or sources while others are based on purely synthetic images or sources generated by computer graphic tools; others are a combination of both. Datasets for the quality assessment of light field content include pristine light field content as well as sources affected by different levels of impairments. The latter are tested subjectively by a panel of viewers and often objective metrics are also calculated. This paper presents a comprehensive comparative review of 33 publicly available datasets that span from content-only datasets to specific task based datasets and quality assessment datasets. While our aim is to review and investigate what each dataset has to offer and which tests had been considered by their proposers, we also take the opportunity to leverage the results of previous studies to identify and discuss the challenges ahead and identify the areas with potential for improvement.INDEX TERMS Light field displays, quality assessment, dataset, subjective tests, objective quality metrics.

show abstract

Section: B Objective Metricsmentioning

confidence: 99%

Datasets for the Quality Assessment of Light Field Imaging: Comparison and Future Directions

Shafiee

Martini

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…These methods usually average the scores obtained by applying the 2D full-reference metrics to single views or sub-aperture images of the LFI. Recently, specifically designed LFI fullreference quality metrics have been proposed [5], [6], [7]. A Log-Gabor-based model is proposed in [5] where the saliency features are extracted from the reference and distorted LFIs, employing the multi-scale and single-scale Gabor wavelet.…”

Section: A Full and Reduced-reference Quality Metricsmentioning

confidence: 99%

“…On the other hand, objective metrics are complex to design and, often, only partially matching the subjective judgment. In full-reference [5], [6], [7] or reduced-reference [8], [9] metrics the full or part of the spatial and angular features of the original LFI are generally used. However the availability of this information may be difficult, especially in a broadcasting scenario, thus motivating the development of no-reference metrics.…”

mentioning

confidence: 99%

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

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.

show abstract

“…Similar to 2D IQA metrics, LF-IQA metrics can be classified into three categories according to their dependence on reference image: Full-Reference (FR), Reduced-Reference (RR), and Blind/No-Reference (NR). The FR LF-IQA metrics (e.g., [4,5,6]) directly evaluate the difference between the distorted LFI and the reference LFI. For instance, MDFM [4] measures the similarities of multi-order derivative features between the distorted and reference LFIs.…”

Section: Introductionmentioning

confidence: 99%

Deeblif: Deep Blind Light Field Image Quality Assessment by Extracting Angular and Spatial Information

Zhang

Tian

Zou

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

In the era of immersive media, the high-dimensional Light Field Image (LFI) puts forward higher requirements for Light Field Image Quality Assessment (LF-IQA). However, currently most existing LF-IQA metrics still rely on sophisticated hand-crafted feature extraction, which fail to predict the quality of LFI accurately. In this paper, we propose a patch-based Deep Blind Light Field image quality assessment metric (abbreviated as DeeBLiF), by employing a two-stream Convolutional Neural Network (CNN) model specifically designed for extracting the angular and spatial information of LFI. Firstly, the spatio-angular patches are generated as input data, which effectively reflect the spatio-angular information of LFI. After that, a two-stream CNN model is exploited to extract the patch features and further predict the patch scores. Finally, all the patch scores are pooled into an overall quality score of LFI. Experimental results on the LFI dataset demonstrate that the proposed DeeBLiF outperforms the state-ofthe-art LF-IQA metrics. The code will be publicly available at https://github.com/ZhengyuZhang96/DeeBLiF.

show abstract

Objective Quality Assessment of Lenslet Light Field Image Based on Focus Stack

Cited by 34 publications

References 63 publications

Datasets for the Quality Assessment of Light Field Imaging: Comparison and Future Directions

Datasets for the Quality Assessment of Light Field Imaging: Comparison and Future Directions

No-Reference Light Field Image Quality Assessment Exploiting Saliency

Deeblif: Deep Blind Light Field Image Quality Assessment by Extracting Angular and Spatial Information

Contact Info

Product

Resources

About