UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

• URL: http://arxiv.org/abs/2005.14354v2
• Date: Sat, 17 Apr 2021 04:40:56 GMT
• Title: UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
• Authors: Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, and Alan C. Bovik
• Abstract summary: Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of content are unpredictable, complicated, and often commingled. Here we contribute to advancing the problem by conducting a comprehensive evaluation of leading VQA models. By employing a feature selection strategy on top of leading VQA model features, we are able to extract 60 of the 763 statistical features used by the leading models. Our experimental results show that VIDEVAL achieves state-of-theart performance at considerably lower computational cost than other leading models.
• Score: 59.13821614689478
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content. Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of UGC content are unpredictable, complicated, and often commingled. Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and VQA model design. By employing a feature selection strategy on top of leading VQA model features, we are able to extract 60 of the 763 statistical features used by the leading models to create a new fusion-based BVQA model, which we dub the \textbf{VID}eo quality \textbf{EVAL}uator (VIDEVAL), that effectively balances the trade-off between VQA performance and efficiency. Our experimental results show that VIDEVAL achieves state-of-the-art performance at considerably lower computational cost than other leading models. Our study protocol also defines a reliable benchmark for the UGC-VQA problem, which we believe will facilitate further research on deep learning-based VQA modeling, as well as perceptually-optimized efficient UGC video processing, transcoding, and streaming. To promote reproducible research and public evaluation, an implementation of VIDEVAL has been made available online: \url{https://github.com/tu184044109/VIDEVAL_release}.

Related papers

• Benchmarking AIGC Video Quality Assessment: A Dataset and Unified Model [54.69882562863726]
  We try to systemically investigate the AIGC-VQA problem from both subjective and objective quality assessment perspectives. We evaluate the perceptual quality of AIGC videos from three dimensions: spatial quality, temporal quality, and text-to-video alignment. We propose a Unify Generated Video Quality assessment (UGVQ) model to comprehensively and accurately evaluate the quality of AIGC videos.
  arXiv  Detail & Related papers  (2024-07-31T07:54:26Z)
• ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment [35.00766551093652]
  We propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model. ReLaX-VQA uses fragments of residual frames and optical flow, along with different expressions of spatial features of the sampled frames, to enhance motion and spatial perception. We will open source the code and trained models to facilitate further research and applications of NR-VQA.
  arXiv  Detail & Related papers  (2024-07-16T08:33:55Z)
• CLIPVQA:Video Quality Assessment via CLIP [56.94085651315878]
  We propose an efficient CLIP-based Transformer method for the VQA problem ( CLIPVQA) The proposed CLIPVQA achieves new state-of-the-art VQA performance and up to 37% better generalizability than existing benchmark VQA methods.
  arXiv  Detail & Related papers  (2024-07-06T02:32:28Z)
• Enhancing Blind Video Quality Assessment with Rich Quality-aware Features [79.18772373737724]
  We present a simple but effective method to enhance blind video quality assessment (BVQA) models for social media videos. We explore rich quality-aware features from pre-trained blind image quality assessment (BIQA) and BVQA models as auxiliary features. Experimental results demonstrate that the proposed model achieves the best performance on three public social media VQA datasets.
  arXiv  Detail & Related papers  (2024-05-14T16:32:11Z)
• Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models [71.06007696593704]
  Blind quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. We conduct a first-of-its-kind computational analysis of VQA datasets via minimalistic BVQA models.
  arXiv  Detail & Related papers  (2023-07-26T06:38:33Z)
• SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement [0.40777876591043155]
  We propose a stack-based framework for video quality assessment (VQA) that outperforms existing state-of-the-art methods on enhanced videos. In addition to proposing the VQA framework for enhanced videos, we also investigate its application on professionally generated content (PGC) Our experiments demonstrate that existing VQA algorithms can be applied to PGC videos, and we find that VQA performance for PGC videos can be improved by considering the plot of a play.
  arXiv  Detail & Related papers  (2023-05-15T07:44:10Z)
• A Deep Learning based No-reference Quality Assessment Model for UGC Videos [44.00578772367465]
  Previous video quality assessment (VQA) studies either use the image recognition model or the image quality assessment (IQA) models to extract frame-level features of videos for quality regression. We propose a very simple but effective VQA model, which trains an end-to-end spatial feature extraction network to learn the quality-aware spatial feature representation from raw pixels of the video frames. With the better quality-aware features, we only use the simple multilayer perception layer (MLP) network to regress them into the chunk-level quality scores, and then the temporal average pooling strategy is adopted to obtain the video
  arXiv  Detail & Related papers  (2022-04-29T12:45:21Z)
• Study on the Assessment of the Quality of Experience of Streaming Video [117.44028458220427]
  In this paper, the influence of various objective factors on the subjective estimation of the QoE of streaming video is studied. The paper presents standard and handcrafted features, shows their correlation and p-Value of significance. We take SQoE-III database, so far the largest and most realistic of its kind.
  arXiv  Detail & Related papers  (2020-12-08T18:46:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.