Enhancing HEVC Compressed Videos with a Partition-Masked Convolutional Neural Network

By Xiaoyi He, Qiang Hu, Xintong Han, Xiaoyun Zhang, Chongyang Zhang, Weiyao Lin. 2018

83 citations recorded by the source corpus.

In this paper, we propose a partition-masked Convolution Neural Network (CNN) to achieve compressed-video enhancement for the state-of-the-art coding standard, High Efficiency Video Coding (HECV). More precisely, our method utilizes the partition information produced by the encoder to guide the quality enhancement process. In contrast to existing CNN-based approaches, which only take the decoded frame as the input to the CNN, the proposed approach considers the coding unit (CU) size information and combines it with the distorted decoded frame such that the degradation introduced by HEVC is reduced more efficiently. Experimental results show that our approach leads to over 9.76% BD-rate saving on benchmark sequences, which achieves the state-of-the-art performance.

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. Trevor Darrell
  3. Dacheng Tao
  4. Jitendra Malik
  5. Xiaogang Wang
  6. Li Fei-Fei
  7. Ming-Hsuan Yang
  8. Andrew Zisserman

In Signal Processing

  1. Oren Rippel
  2. Lubomir D. Bourdev
  3. Steve Branson

Citations in the corpus