QoE Assessment for Prompt-Based Videos: Acceptability and Quality Factors

The development of video technology and consumer devices has resulted in a boom of user-generated clips on various social networking services. The recent OpenAI Sora model can revolutionize the production of consumer videos, simply using prompts. However, videos generated using prompts usually contain new artifacts induced by generative AI. In addition, while various researchers have conducted extensive quality assessments on AI-generated videos, studies specifically focused on the Quality of Experience (QoE) of such videos remain limited. Given the increasing role of generative AI in media production, it is crucial to evaluate how users perceive and interact with these videos. In this article, we present the first study on the acceptability assessment (also known as customer satisfaction score) of prompt-based videos, which are generated by OpenAI Sora. In the assessment, subjects were asked to rate the video's alignment, perceptual quality, aesthetic quality, and whether the video is acceptable or not. The results show that, though the videos are visually impressive, not many videos are actually acceptable to users. Also, the relationship between acceptability and the other quality aspects is complex and nonlinear. Especially, it is found that perceptual quality contributes the most to acceptability, while alignment is the least significant contributor. In addition, we identify over ten quality factors that cause low acceptability of prompt-based videos. Finally, results from objective evaluation show that the performances of current quality metrics are insufficient to automatically monitor the quality and alignment of prompt-based videos.

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Publication Details

Journal
IEEE Consumer Electronics Magazine
Published
2024-12-18
DOI
https://doi.org/10.1109/mce.2024.3519777
Citations
2
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
0.50
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article

QoE Assessment for Prompt-Based Videos: Acceptability and Quality Factors

Chibuike Onuoha, Shihao Luo, Truong Cong Thang, Trương Thu Hương et al.
2 citations
IEEE Consumer Electronics Magazine
Image and Video Quality Assessment
0.50
article

QoE Assessment for Prompt-Based Videos: Acceptability and Quality Factors

Chibuike Onuoha, Shihao Luo, Truong Cong Thang, Trương Thu Hương, Nguyen Duc Nam, Vu Hai Dang
article en
2 citations

Abstract

The development of video technology and consumer devices has resulted in a boom of user-generated clips on various social networking services. The recent OpenAI Sora model can revolutionize the production of consumer videos, simply using prompts. However, videos generated using prompts usually contain new artifacts induced by generative AI. In addition, while various researchers have conducted extensive quality assessments on AI-generated videos, studies specifically focused on the Quality of Experience (QoE) of such videos remain limited. Given the increasing role of generative AI in media production, it is crucial to evaluate how users perceive and interact with these videos. In this article, we present the first study on the acceptability assessment (also known as customer satisfaction score) of prompt-based videos, which are generated by OpenAI Sora. In the assessment, subjects were asked to rate the video's alignment, perceptual quality, aesthetic quality, and whether the video is acceptable or not. The results show that, though the videos are visually impressive, not many videos are actually acceptable to users. Also, the relationship between acceptability and the other quality aspects is complex and nonlinear. Especially, it is found that perceptual quality contributes the most to acceptability, while alignment is the least significant contributor. In addition, we identify over ten quality factors that cause low acceptability of prompt-based videos. Finally, results from objective evaluation show that the performances of current quality metrics are insufficient to automatically monitor the quality and alignment of prompt-based videos.

IEEE Consumer Electronics MagazineVol. 14(5)
University of Aizu (JP), Hanoi University of Science and Technology (VN)
Openalex Percentile: Top 33%
Image and Video Quality Assessment
0.50
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