Image Quality Assessment by Comparing CNN Features between Images
By Seyed Ali Amirshahi, Marius Pedersen, Stella X. Yu. 2016
73 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Phillip Isola
- Richard Zhang
- Alexei A. Efros
- Eli Shechtman
- Oliver Wang
- Antonio Torralba
- Andrew Zisserman
- Andrea Vedaldi
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Richard Zhang
- Alexei A. Efros
- Ming-Hsuan Yang
- Trevor Darrell
- Eli Shechtman
- Dacheng Tao
- Jitendra Malik
In Industrial and Manufacturing Engineering
In Media Technology
- Richard Zhang
- Eli Shechtman
- Alexei A. Efros
- Phillip Isola
- Jun-Yan Zhu
- Oliver Wang
- Daniel Cohen-Or
- Antonio Torralba
Citations in the corpus
- The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (2018, methodology): “[3] propose techniques involving leveraging internal activations of deep networks (VGG and AlexNet, respectively) along with additional multiscale post-processing.”
- PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, methodology): “…visual system (HVS) such as contrast sensitivity [22], high-level structural acuity [52], and masking [48, 51], or use other statistics/features [3,4,14,15,38,44–46, 53, To address these limitations, some have proposed IQA methods based on machine learning to learn more sophisticated models…”