About
Video Compression is a process of reducing the size of an image or video file by exploiting spatial and temporal redundancies within an image or video frame and across multiple video frames. The ultimate goal of a successful Video Compression system is to reduce data volume while retaining the perceptual quality of the decompressed data.
Source: Adversarial Video Compression Guided by Soft Edge Detection
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Datasets
Latest papers with code
Efficient Video Compression via Content-Adaptive Super-Resolution
• AdaptiveVC/SRVC
• 
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.
Lossless compression with state space models using bits back coding
We generalize the 'bits back with ANS' method to time-series models with a latent Markov structure.
B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction
In this paper, we design a neural network to enhance the quality of the compressed frame by leveraging the block information, called B-DRRN (Deep Recursive Residual Network with Block information).
Ultra-low bitrate video conferencing using deep image animation
In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications.
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
• InterDigitalInc/CompressAI
• 
This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs.
Hierarchical Autoregressive Modeling for Neural Video Compression
Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.
OpenDVC: An Open Source Implementation of the DVC Video Compression Method
At the time of writing this report, several learned video compression methods are superior to DVC, but currently none of them provides open source codes.
Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model
The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.
Remote Photoplethysmography: Rarely Considered Factors
Second factor is rolling shutter effect which generates tiny phase shift of the same PPG signal in different parts of the frame caused by progressive scanning.
M-LVC: Multiple Frames Prediction for Learned Video Compression
CVPR 2020
• JianpingLin/M-LVC_CVPR2020
•
To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well.