  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%.
