Disentangling Aesthetic and Technical Effects for Video Quality Assessment of User Generated Content

Wu, Haoning; Liao, Liang; Chen, Chaofeng; Hou, Jingwen; Wang, Annan; Sun, Wenxiu; Yan, Qing; Wang, Lin

doi:10.48550/arxiv.2211.04894

Cited by 3 publications

(2 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent NR-VQA works using deep networks include Patch-VQ (Ying et al, 2021), FAST-VQA (Wu et al, 2022a), HVS-5M (Zhang et al, 2022), DOVER (Wu et al, 2022b). Patch-VQ introduced a novel approach for VQA by creating a large-scale UGC video database with subjective ratings for full videos and spatiotemporal crops of the videos.…”

Section: No Reference Objective Video Quality Assessmentmentioning

confidence: 99%

Perceptual video quality assessment: the journey continues!

Saha,

Pentapati,

Shang

et al. 2023

Front. Signal Process.

View full text Add to dashboard Cite

Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future.

show abstract

Section: No Reference Objective Video Quality Assessmentmentioning

confidence: 99%

Perceptual video quality assessment: the journey continues!

Saha,

Pentapati,

Shang

et al. 2023

Front. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Conclusion and Future Works123 Disentangle Aesthetic Factors for Visual Quality Assessment Generally, the production process of multimedia contents involves both human and machines[107], and therefore, the visual pleasantness of the final image can be affected by both human factors and machine factors. On the one hand, different cameras and display devices can produce images of different levels of visual pleasantness due to the imaging process implemented with different software and hardware.…”

mentioning

confidence: 99%