24848

Efficient Video Compression via Content-Adaptive Super-Resolution

Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh
MIT CSAIL
arXiv:2104.02322 [cs.CV], (6 Apr 2021)

@misc{khani2021efficient,

   title={Efficient Video Compression via Content-Adaptive Super-Resolution},

   author={Mehrdad Khani and Vibhaalakshmi Sivaraman and Mohammad Alizadeh},

   year={2021},

   eprint={2104.02322},

   archivePrefix={arXiv},

   primaryClass={cs.CV}

}

Video compression is a critical component of Internet video delivery. Recent work has shown that deep learning techniques can rival or outperform human-designed algorithms, but these methods are significantly less compute and power-efficient than existing codecs. This paper presents a new approach that augments existing codecs with a small, content-adaptive super-resolution model that significantly boosts video quality. Our method, SRVC, encodes video into two bitstreams: (i) a content stream, produced by compressing downsampled low-resolution video with the existing codec, (ii) a model stream, which encodes periodic updates to a lightweight super-resolution neural network customized for short segments of the video. SRVC decodes the video by passing the decompressed low-resolution video frames through the (time-varying) super-resolution model to reconstruct high-resolution video frames. Our results show that to achieve the same PSNR, SRVC requires 16% of the bits-per-pixel of H.265 in slow mode, and 2% of the bits-per-pixel of DVC, a recent deep learning-based video compression scheme. SRVC runs at 90 frames per second on a NVIDIA V100 GPU.
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