Reading: Kuanar JCCSP’19 — Object Detection Approach for Video Coding (Fast HEVC Prediction)

Original article was published on Deep Learning on Medium

Reading: Kuanar JCCSP’19 — Object Detection Approach (Fast HEVC Prediction)

Encoding Time Saving Up to 66.89%, BD-Rate Loss Low to 1.31%, Outperforms Li ICME’17

In this paper, “Adaptive CU Mode Selection in HEVC Intra Prediction: A Deep Learning Approach” (Kuanar JCCSP’19), by University of Texas at Arlington, and University of Texas at Dallas, is briefly presented. In this paper:

  • CNN-based Object Detection Network is used which includes Region Proposal Network (RPN).
  • Regions are classified as homogeneous object, big object, granular texture, and small object.
  • According to the object type, different fast approaches are applied.

This is a paper in 2019 JCCSP (Springer Journal of Circuits, Systems, and Signal Processing) with impact factor of 1.922. (Sik-Ho Tsang @ Medium)