Visual Quality Assessment for Perceptually Encrypted Light Field Images

Wen, Wenying; Wei, Kangkang; Fang, Yuming; Zhang, Yushu

doi:10.1109/tcsvt.2020.3026817

Cited by 16 publications

(7 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their approach on scene reconstruction involved using a high but limited number of high resolution light field images captured with a DSLR camera mounted on a computer controlled 1.5 meter long Zaber T-LST1500D 8 linear stage. 100 images were acquired from 8 https://www.zaber.com/products/linear-stages/T-LST/details/ T-LST1500D/features FIGURE 5. Shaded 3D mesh of an outdoor scene generated from triangulating individual depth maps [33].…”

Section: B Task Based Datasetsmentioning

confidence: 99%

“…We finally focus on the limitations of existing datasets and provide some recommendations for the design of new datasets that can support the scientific community in the design of novel compression, transmission, and quality assessment algorithms for light field content. Some earlier papers, providing results for objective quality metrics or specific tasks performed on light fields, listed some datasets; however, the purpose was not to describe and compare them and only a limited number of datasets was considered in each of these papers [6], [7], [8], [9], [10]. A review of some of the existing datasets was provided a few years ago by Ellahi et al [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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 Task Based Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

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

“…Other Applications. Besides the application mentioned above, there are other promising distortion-specific BIQAs designed for emerging computer vision tasks, such as superresolution [109], segmentation [66], dehazing [6], deraining [87], aesthetic [15], encryption [84], light-field [73], underwater [108], 3D geometry [81], etc. Due to the length of the article, we will not discuss other types of distortionspecific BIQAs here.…”

Section: Distortion-specific Biqasmentioning

confidence: 99%

Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images

Wang¹,

Xu²,

Xu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals, which has been widely used to monitor product and service quality in low-light applications, covering smartphone photography, video surveillance, autonomous driving, etc. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns, where human visual perception is simultaneously reflected by multiple sensory information (e.g., sight and hearing). In this article, we present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score. To investigate the multimodal mechanism, we first establish a multimodal low-light image quality (MLIQ) database with authentic low-light distortions, containing image and audio modality pairs. Further, we specially design the key modules of BMQA, considering multimodal quality representation, latent feature alignment and fusion, and hybrid self-supervised and supervised learning. Extensive experiments show that our BMQA yields state-of-the-art accuracy on the proposed MLIQ benchmark database. In particular, we also build an independent single-image modality Dark-4K database, which is used to verify its applicability and generalization performance in mainstream unimodal applications. Qualitative and quantitative results on Dark-4K show that BMQA achieves superior performance to existing BIQA approaches as long as a pre-trained quality semantic description model is provided. The proposed framework and two databases as well as the collected BIQA methods and evaluation metrics are made publicly available.

show abstract

“…In some applications like depth estimation [24], [27], [28], [29], super-resolution [30], [31], [32], saliency detection [33], [34], [35] and view synthesis [36], [37], LF datasets have been widely used with remarkable results. While in other applications like quality assessment [38], [39], video processing [40] and intrinsic decomposition [41], this attempt still is in preliminary stage with initial success. The summary of these LF datasets is shown in Table I.…”

Section: A Light Field Datasetsmentioning

confidence: 99%

UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes

Sheng

Cong

Yang

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

104

View full text Add to dashboard Cite

As one of the fundamental technologies for scene understanding, semantic segmentation has been widely explored in the last few years. Light field cameras encode the geometric information by simultaneously recording the spatial information and angular information of light rays, which provides us with a new way to solve this issue. In this paper, we propose a highquality and challenging urban scene dataset, containing 1074 samples composed of real-world and synthetic light field images as well as pixel-wise annotations for 14 semantic classes. To the best of our knowledge, it is the largest and the most diverse light field dataset for semantic segmentation. We further design two new semantic segmentation baselines tailored for light field and compare them with state-of-the-art RGB, video and RGB-D-based methods using the proposed dataset. The outperforming results of our baselines demonstrate the advantages of the geometric information in light field for this task. We also provide evaluations of super-resolution and depth estimation methods, showing that the proposed dataset presents new challenges and supports detailed comparisons among different methods. We expect this work inspires new research direction and stimulates scientific progress in related fields. The complete dataset is available at https://github.com/HAWKEYE-Group/UrbanLF.

show abstract

Visual Quality Assessment for Perceptually Encrypted Light Field Images

Cited by 16 publications

References 49 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

Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images

UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes

Contact Info

Product

Resources

About