Predicting visible flicker in temporally changing images

Dénes, György; Mantiuk, Rafał

doi:10.2352/issn.2470-1173.2020.11.hvei-233

Cited by 1 publication

(1 citation statement)

References 13 publications

(20 reference statements)

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“…Since most of the image quality metrics have not been calibrated or validated on the distortions specific to novel view synthesis, their predictions could be too noisy to quantify perceived quality. ➋ The evaluation protocol lacks assessment on video sequences, which can reveal temporal artifacts and subtle distortions, such as flickering or floating ghost images, that are easily noticeable in video but difficult to spot in static images [CAD19; DM20; LAK*16; MDC*21a]. This issue is compounded by the limited nature of commonly used NVS datasets, which do not have reference videos for evaluating NVS methods.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front‐Facing Views

Liang,

Wu,

Hanji

et al. 2024

Computer Graphics Forum

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Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research on how NVS methods perform with respect to perceived video quality. We present the first study on perceptual evaluation of NVS and NeRF variants. For this study, we collected two datasets of scenes captured in a controlled lab environment as well as in‐the‐wild. In contrast to existing datasets, these scenes come with reference video sequences, allowing us to test for temporal artifacts and subtle distortions that are easily overlooked when viewing only static images. We measured the quality of videos synthesized by several NVS methods in a well‐controlled perceptual quality assessment experiment as well as with many existing state‐of‐the‐art image/video quality metrics. We present a detailed analysis of the results and recommendations for dataset and metric selection for NVS evaluation.

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Section: Introductionmentioning

confidence: 99%