Search Results for author: Troy Chinen

Found 4 papers, 0 papers with code

An Unsupervised Information-Theoretic Perceptual Quality Metric

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.

Image Compression Image Quality Assessment +2

Towards a Semantic Perceptual Image Metric

no code implementations1 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.

Image Quality Assessment

Spatially adaptive image compression using a tiled deep network

no code implementations7 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.

Image Compression

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

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.

Image Compression MS-SSIM +1

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