Adversarial Distortion for Learned Video Compression
- URL: http://arxiv.org/abs/2004.09508v3
- Date: Fri, 18 Jun 2021 18:42:25 GMT
- Title: Adversarial Distortion for Learned Video Compression
- Authors: Vijay Veerabadran, Reza Pourreza, Amirhossein Habibian, Taco Cohen
- Abstract summary: We present a deep adversarial learned video compression model that minimizes an auxiliary adversarial distortion objective.
Our experiments using a state-of-the-art learned video compression system demonstrate a reduction of perceptual artifacts and reconstruction of detail lost especially under extremely high compression.
- Score: 17.721259583120396
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we present a novel adversarial lossy video compression model.
At extremely low bit-rates, standard video coding schemes suffer from
unpleasant reconstruction artifacts such as blocking, ringing etc. Existing
learned neural approaches to video compression have achieved reasonable success
on reducing the bit-rate for efficient transmission and reduce the impact of
artifacts to an extent. However, they still tend to produce blurred results
under extreme compression. In this paper, we present a deep adversarial learned
video compression model that minimizes an auxiliary adversarial distortion
objective. We find this adversarial objective to correlate better with human
perceptual quality judgement relative to traditional quality metrics such as
MS-SSIM and PSNR. Our experiments using a state-of-the-art learned video
compression system demonstrate a reduction of perceptual artifacts and
reconstruction of detail lost especially under extremely high compression.
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