谷歌在常用的763个视频统计特征中使用特征选择策略挑选出60个,创建了一种基于融合的BVQA模型(VIDEVAL),有效平衡了性能和效率的权衡。 实验结果显示该方法在相比其他SOTA方法,精度更高、速度更快。谷歌同时也为UGC-VQA问题定义了一个基准测试,有助于该领域算法的评估和发展。 同时VIDEVAL的代码已开源。 作者| Zhengz...
不同模型在三个数据集上的效果如上表所示,分析可以得到: (1)本文所提出的MD-VQA在LIVE-WC、YT-UGC+和TaoLive三个数据集上的SRCC值方面分别获得第一名这证明了其预测压缩 UGC 视频质量水平的有效性。 (2)基于手工提取特征的方法(BRISQUE、TLVQM 和 VIDEVAL)明显不如基于深度学习的方法(VSFA、PVQ、BVQA、...
数据集 无参考数据集 - 一无所知小白龙 - 博客园 谷歌推出UGC内容的盲视频质量评估方法和基准测试 - 知乎 UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content 针对用户生成内容
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,...
Comparing with SOTA FR-VQA metrics: 对于直接预测压缩损伤视频MOS分的NR方法,火山引擎多媒体实验室提出的NR框架在所有评价指标中排名第一;对于预测参考视频和损伤视频质量差异DMOS分值的FR方法,火山引擎多媒体实验室提出的FR框架在预测单调性(即SROCC和KROCC)方面排名第一,在预测精度(即PLCC和RMSE)方面排名第二。同...
The UGC Live VQA database-TaoLive 1. Introduction With the rapid development of social media applications and the advancement of video shooting and processing technologies, more and more ordinary people are willing to tell their stories, share their experiences, and have their voice heard on social...
demo_eval_BVQA_feats_one_dataset.py You need to specify the parameters demo_eval_BVQA_feats_all_combined.py You need to specify the parameters If you use this code for your research, please cite our papers. Contact Zhengzhong TU,zhengzhong.tu@utexas.edu ...
A Feature Attention (FA) module is further proposed to help the model focus on important parts of the video. The experimental results show that the proposed model achieves good performance on mainstream subjective UGC video quality databases, indicating the effectiveness on UGC VQA.Yang, Zike...
或者关键信息,包括踢、扔、抛物体等。视频质量分析VQA视频质量分析(Video Quality Analysis)是通过深度卷积神经网络算法识别视频画面质量,将视频画面的质量进行归类,从而过滤出清晰的高质量视频。视频OCR:视频OCR(Video Optical Character 来自:百科 查看更多 → ...
The reason is that the proposed model and the model in [29 ] calculate the chunk-level quality score and the effect of adjacent frames are considered in the quality-aware features (i.e. motion features), while other VQA models [ 13 ] [12 ] calculate the frame-level quality scores, whic...