A modified PSNR metric based on HVS for quality assessment of color images
By Prateek Gupta, Priyanka Srivastava, Satyam Bhardwaj, Vikrant Bhateja. 2011
132 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Radu Timofte
- Dacheng Tao
- Chen Change Loy
- Trevor Darrell
- Andrew Zisserman
- Andrea Vedaldi
In Media Technology
- Ferenc Huszár
- Radu Timofte
- Ming-Hsuan Yang
- Chao Dong
- Lucas Theis
- Wenzhe Shi
- Jose Caballero
- Sunghyun Cho
In Evaluation & Benchmarks
- Ferenc Huszár
- Jose Caballero
- Ki Sang Hong
- Michele Covell
- George Toderici
- Lucas Theis
- Sung Jin Hwang
- Seong-Jin Park
Citations in the corpus
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, background): “However, the ability of MSE (and PSNR) to capture perceptually relevant differences, such as high texture detail, is very limited as they are defined based on pixel-wise image differences [52, 49, 21].”
- Full Resolution Image Compression with Recurrent Neural Networks (2016, methodology): “When training on the High Entropy dataset, The One Shot version of LSTM had the highest MS-SSIM, but the worst PSNR-HVS.”
- SRFeat: Single Image Super-Resolution with Feature Discrimination (2018, background): “However, it is well-known that measuring pixel-wise difference can hardly capture perceptual differences between images [49, 48, 17], thus higher PSNR does not necessarily lead to a per-”