Reading: Kang CVPR’14 — Convolutional Neural Networks for No-Reference (Image Quality Assessment)

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


Reading: Kang CVPR’14 — Convolutional Neural Networks for No-Reference (Image Quality Assessment)

Outperforms CORNIA, BRISQUE, FSIM, SSIM & PSNR

In this story, “Convolutional Neural Networks for No-Reference Image Quality Assessment” (Kang CVPR’14), by University of Maryland, and NICTA and ANU, is presented. I read this paper because recently I need to study/work on IQA/VQA (Image Quality Assessment/Video Quality Assessment). In this story:

  • A Convolutional Neural Network (CNN) is designed to accurately predict image quality without a reference image, i.e. No-Reference Approach.
  • The network cdonsists of one convolutional layer with max and min pooling, two fully connected layers and an output node.

This is a paper in 2014 CVPR with over 500 citations. (Sik-Ho Tsang @ Medium)