In the design of action recognition models, the quality of videos is an important issue; however, the trade-off between the quality and performance is often ignored. In general, action recognition models are trained on high-quality videos, hence it is not known how the model performance degrades when tested on low-quality videos, and how much the quality of training videos affects the performance. The issue of video quality is important, however, it has not been studied so far. The goal of this study is to show the trade-off between the performance and the quality of training and test videos by quantitative performance evaluation of several action recognition models for transcoded videos in different qualities. First, we show how the video quality affects the performance of pre-trained models. We transcode the original validation videos of Kinetics400 by changing quality control parameters of JPEG (compression strength) and H.264/AVC (CRF). Then we use the transcoded videos to validate the pre-trained models. Second, we show how the models perform when trained on transcoded videos. We transcode the original training videos of Kinetics400 by changing the quality parameters of JPEG and H.264/AVC. Then we train the models on the transcoded training videos and validate them with the original and transcoded validation videos. Experimental results with JPEG transcoding show that there is no severe performance degradation (up to -1.5%) for compression strength smaller than 70 where no quality degradation is visually observed, and for larger than 80 the performance degrades linearly with respect to the quality index. Experiments with H.264/AVC transcoding show that there is no significant performance loss (up to -1%) with CRF30 while the total size of video files is reduced to 30%.
翻译:在行动识别模型的设计中,视频质量是一个重要问题;然而,质量和性能之间的权衡往往被忽略。一般而言,行动识别模型是用高质量的视频培训的,因此不清楚模型性能在低质量视频测试时是如何退化的,培训视频的质量如何影响性能。但是,视频质量问题至今尚未研究。本研究的目的是通过对不同品质的转码视频的若干行动识别模型进行定量绩效评估,来显示业绩与培训和测试视频质量之间的权衡。首先,我们展示了视频质量认证模型是如何影响预培训模型的绩效的。我们通过改变JPEG(压力强度)和H.264/AVC(压力)的质量控制参数,将最初验证视频质量视频的校正性能转换为80-30,然后将原性能认证模型与原性能认证结果转换为80-C。我们通过将原性能认证模型转换为80-GEG和H.26AVC,然后将原性能测试结果转换为30。