DeepCoder: A deep neural network based video compression

By Tong Chen, Qiu Shen, Tao Yue, Xun Cao, Zhan Ma. 2017

120 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Wanli Ouyang
4. Trevor Darrell
5. Dacheng Tao
6. Yi Yang
7. Ming-Hsuan Yang
8. Jitendra Malik

In Signal Processing

1. Guo Lu
2. Shanshe Wang
3. Oren Rippel
4. Lubomir D. Bourdev
5. Shiqi Wang
6. Siwei Ma
7. Wanli Ouyang
8. Xinfeng Zhang

Citations in the corpus

• Learned Video Compression (2018, methodology): “To our knowledge, the only pre-existing end-to-end ML-based video compression approachs are [52, 8, 16].”
• Deep Probabilistic Video Compression (2018, unknown): “Chen et al. (2017) and Chen et al. (2018) use a deep neural architecture to predict the most likely frame with a modified form of block motion prediction and store residuals in a lossy representation.”
• DVC: An End-To-End Deep Video Compression Framework (2018, methodology): “For the video compression task, a lot of DNN based methods have been proposed for intra prediction and residual coding[13], mode decision [22], entropy coding [30], post-processing [23].”
• Image and Video Compression With Neural Networks: A Review (2019, background): “proposed a combination of several CNN networks called DeepCoder which achieved similar perceptual quality with low-profiled x264 encoder [114].”