Can No-reference features help in Full-reference image quality estimation?

Dutta, Saikat; Das, Sourya Dipta; Shah, Nisarg

doi:10.48550/arxiv.2203.00845

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The possible use of no-reference metrics concerns a number of research areas described in the literature, such as the NAVE metric for autoencoders [21], NR-GVQM for gaming [22], or the H.264/AVC-based bitstream no-reference video quality metric employing a multiway Partial Least Squares Regression (PLSR) [23]. Hybrid models, utilizing both Full-reference and No-reference feature extraction to assess objective technical quality was also published [24]. The selection of hybrid or no-reference video quality metrics in planned future research will be the subject of a separate analysis beyond the scope of this publication.…”

Section: Research Datasetmentioning

confidence: 99%

Impact of the Stimulus Presentation Structure on Subjective Video Quality Assessment

Konaszyński,

Juszka,

Leszczuk

2023

Electronics

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A study of video quality perceived by user (Quality of Experience, QoE) was conducted with an examination of whether and how the order and structure of the video sequence presentation affects subjective assessment. For this purpose, the influence of content variability/repeatability, the quality of the preceding sequence, and the sequence order were analyzed. Observations on the correlation of QoE with the micro-structure of sequence presentation are described, which can be the basis for hypotheses of the dependence of QoE assessment on the abovementioned factors. The observed relationships regarding the influence of the number of video repetitions and the impact of the predecessor’s quality on subjective evaluation are consistent with research work on the influence of the order/arrangement and structure of research stimuli on the results of subjective evaluations. Areas for further research are indicated, including relating obtained results in the area of QoE to other cognitive sciences, such as psychological, medical, and economic sciences.

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Section: Research Datasetmentioning

confidence: 99%

Impact of the Stimulus Presentation Structure on Subjective Video Quality Assessment

Konaszyński,

Juszka,

Leszczuk

2023

Electronics

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“…The Horizon team proposes a Multi-branch Image Quality Assessment Network [21] that consists of three parallel branches: (1) the full-reference pre-trained (FRP) branch, (2) the full-reference non-pretrained (FRNP) branch and (3) the no-reference (NR) branch. Both distorted and reference images are fed as input for the full-reference branches (FRP and FRNP), whereas in the no-reference branch, only the distorted image is provided as input.…”

Section: Team Horizonmentioning

confidence: 99%

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

Gu¹,

Cai²,

Chen³

et al. 2022

Preprint

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This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate stateof-the-art performance.

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Perceived rain dynamics on hydrophilic/hydrophobic lens surfaces and their influences on vehicle camera performance

Pao,

Li,

Agelin-Chaab

2024

Trans. Can. Soc. Mech. Eng.

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Cameras are increasingly used in modern vehicles equipped with advanced driver assistance systems (ADAS) to collect environmental information. Cameras suffer performance degradation when driving in adverse weather conditions, such as rain, as precipitation droplets impact the camera lens and cause obstruction and blurring of the vision. The relationships between image quality, object detection accuracy, and surface wettability of camera lenses are investigated. This paper applies a previously developed evaluation procedure for wind tunnel testing with simulated adverse driving and rain conditions. Realistic rain characteristics perceived by a moving vehicle at different driving speeds are simulated using a novel rain simulation system implemented into a wind tunnel. Moreover, an emphasis is put on comparing the use of hydrophilic and hydrophobic surfaces to provide insights into material selection when designing camera lenses for ADAS. It is found that droplet dynamics, such as size, velocity, shape, and motion can impact the camera image quality and, subsequently, object detection accuracy. This paper demonstrates the use of various materials and evaluation metrics, and their implications from a practical perspective when subjected to realistic driving-in-rain scenarios. The results suggest that the use of hydrophobic lenses promotes better performance over hydrophilic lenses in most cases with exceptions.

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Can No-reference features help in Full-reference image quality estimation?

Cited by 3 publications

References 25 publications

Impact of the Stimulus Presentation Structure on Subjective Video Quality Assessment

Impact of the Stimulus Presentation Structure on Subjective Video Quality Assessment

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

Perceived rain dynamics on hydrophilic/hydrophobic lens surfaces and their influences on vehicle camera performance

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