no code implementations • NeurIPS 2020 • Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen
We show that PIM is competitive with supervised metrics on the recent and challenging BAPPS image quality assessment dataset and outperforms them in predicting the ranking of image compression methods in CLIC 2020.
no code implementations • 1 Aug 2018 • Troy Chinen, Johannes Ballé, Chunhui Gu, Sung Jin Hwang, Sergey Ioffe, Nick Johnston, Thomas Leung, David Minnen, Sean O'Malley, Charles Rosenberg, George Toderici
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.
no code implementations • 7 Feb 2018 • David Minnen, George Toderici, Michele Covell, Troy Chinen, Nick Johnston, Joel Shor, Sung Jin Hwang, Damien Vincent, Saurabh Singh
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
no code implementations • CVPR 2018 • Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Troy Chinen, Sung Jin Hwang, Joel Shor, George Toderici
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM.