Recognizing Compressed Videos: Challenges and Promises

Reza Pourreza, Amir Ghodrati, Amirhossein Habibian; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0


This paper studies the effect of quality degradation, caused by lossy video compression, on video recognition. We investigate how the state of the art video enhancement restores the video quality needed for an effective video recognition. Furthermore, we study the impact of various enhancement objectives, namely pixel-level, feature-level, and adversarial, on action recognition performance. Our experiments demonstrate that the models trained on pixel-level loss perform well in terms of visual quality but they hurt the accuracy of action recognition due to over smoothing discriminative features. On the other hand, models trained on perceptual and adversarial loss types not only generate better perceptual quality but also further improve the action recognition performance.

Related Material

author = {Pourreza, Reza and Ghodrati, Amir and Habibian, Amirhossein},
title = {Recognizing Compressed Videos: Challenges and Promises},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}