Reading: Kim TCSVT’19 — Fast CU Depth Decision Using Neural Network (Fast HEVC Prediction)

Original article was published on Artificial Intelligence on Medium

Reading: Kim TCSVT’19 — Fast CU Depth Decision Using Neural Network (Fast HEVC Prediction)

LeNet-Like Architecture, 61.77% Average Time Reduction, 3.91% Increase in BD-Rate, Outperforms Liu ISCAS’16

In this story, “Fast CU Depth Decision for HEVC Using Neural Networks” (Kim TCSVT’19), by Yonsei University, is presented. I read this because I work on video coding research. In this paper:

  • A LeNet-Like Architecture is used, which consists of the convolution and pooling layers for analyzing the image property of the CU.
  • The feature map is then concatenated with the vector data and trained by fully connected layers in order to analyze the encoding property of the CU.

This is a paper in 2019 TCVST where TCSVT has a high impact factor of 4.046. (Sik-Ho Tsang @ Medium)