Subjective and Objective Audio-Visual Quality Assessment for User Generated Content

Cao, Yuqin; Min, Xiongkuo; Sun, Weiqiang; Zhai, Guangtao

doi:10.1109/tip.2023.3290528

Cited by 14 publications

(2 citation statements)

References 42 publications

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“…A similar method could be applied to video quality assessment, but we must bear in mind the computational complexity of such solutions and their suitability for real-time QoS management. Recently, some researchers focused their attention on User-Generated Content (UGC), like Yu et al [37] or Cao et al [38]. In both works, the authors mainly concentrated on constructing the UGC databases suitable for future QoE research.…”

Section: Related Workmentioning

confidence: 99%

“…In both works, the authors mainly concentrated on constructing the UGC databases suitable for future QoE research. Yet, both groups of researchers also proposed VQA models that can be used for QoE assessment [38] or for learning qualityaware audio and visual feature representations in the temporal domain [38]. Apart from UGC videos, another specific category of videos, demanding a unique approach to quality assessment, are nighttime videos, analyzed in [39], where the authors proposed a blind nighttime video quality assessment model based on feature fusion.…”

Section: Related Workmentioning

confidence: 99%

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FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

Mrvelj,

Matulin

2023

Future Internet

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In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

Mrvelj,

Matulin

2023

Future Internet

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Perceptual video quality assessment: a survey

Min,

Duan,

Sun

et al. 2024

Sci. China Inf. Sci.

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Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of Internet communication and cloud service technology, video content and traffic are growing exponentially, which further emphasizes the requirement for accurate and rapid assessment of video quality. Therefore, numerous subjective and objective video quality assessment studies have been conducted over the past two decades for both generic videos and specific videos such as streaming, user-generated content, 3D, virtual and augmented reality, high dynamic range, high frame rate, audio-visual, etc. This survey provides an up-to-date and comprehensive review of these video quality assessment studies. Specifically, we first review the subjective video quality assessment methodologies and databases, which are necessary for validating the performance of video quality metrics. Second, the objective video quality assessment measures for general purposes are categorized and surveyed according to the methodologies utilized in the quality measures. Third, we overview the objective video quality assessment measures for specific applications and emerging topics. Finally, the performance of the state-of-the-art video quality assessment measures is compared and analyzed. This survey provides a systematic overview of both classical works and recent progress in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.

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Dual-graph hierarchical interaction network for referring image segmentation

Shi,

Wu,

et al. 2023

Displays

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Subjective and Objective Audio-Visual Quality Assessment for User Generated Content

Cited by 14 publications

References 42 publications

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

Perceptual video quality assessment: a survey

Dual-graph hierarchical interaction network for referring image segmentation

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