Better Compression with Deep Pre-Editing

Hossein Talebi     Damien Kelly    Xiyang Luo    Ignacio Garcia-Dorado
Feng Yang    Peyman Milanfar    Michael Elad

Google Research

IEEE Transactions on Image Processing 2020

Teaser image


Could we compress images via standard codecs while avoiding artifacts? The answer is obvious -- this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.




Talebi, Hossein, Damien Kelly, Xiyang Luo, Ignacio Garcia Dorado, Feng Yang, Peyman Milanfar, and Michael Elad. "Better Compression with Deep Pre-Editing". arXiv preprint arXiv:2002.00113 2020.

  title={Better Compression with Deep Pre-Editing},
  author={Talebi, Hossein and Kelly, Damien and Luo, Xiyang and Garcia-Dorado, Ignacio
          and Yang, Feng and Milanfar, Peyman and Elad, Michael},
  journal={arXiv preprint arXiv:2002.00113},