Reading: CNN-SENet — Fast Depth Intra Coding (Fast 3D-HEVC)

Original article was published by Sik-Ho Tsang on Artificial Intelligence on Medium

Reading: CNN-SENet — Fast Depth Intra Coding (Fast 3D-HEVC)

20.9& Encoding Time Reduction Without Any Significant Loss

3D-HEVC (DIBR: Depth Image Based Rendering)

In this story, Fast Depth Inra Coding based on Layer-classification and CNN for 3D-HEVC (CNN-SENet), is presented. I read this because I work on video coding research. This paper only got 1 page, that means there is not much details. In this paper:

  • A convolutional neural network (CNN) scheme based on layer-classification for fast depth intra coding is designed to determine the smoothest depth map.
  • Then, a CNN network incorporating SENet (CNN-SENet) structure is designed and trained.
  • Finally, the layer-classification model and the CNN-SENet network are combined to predict the coding unit (CU) partition of all coding units (CUs) for depth map.