Artifacts reduction for very low bitrate image compression with generative adversarial networks Image compression at very low bitrate has known a new dawn since the democratization of convolutional neural networks (CNNs). A promising approach consists in using an xc2xab endtoend xc2xbb neural networkbased compression scheme, performing both coding and decoding phases. Such techniques lead to perceptually convincing results, notably with GANbased architectures. However, due to the nature of GAN, alongside being computationally expensive on the encoder side, such schemes cannot ensure the preservation of fine details, such as small digits or letters on an ID card. To overcome this limitation, we propose a new model, specifically trained to reduce artifacts resulting from strong lossy compressions. An advantage of the proposed approach comes from the fact that it can be interpreted as a postprocessing step that can easily be added to any compression scheme without modifying the codec. Experimental results show that our model perceptually outperforms the stateoftheart compression standards for very low bitrates.

Extreme Image Coding via Multiscale Autoencoders with Generative Adversarial Optimization We propose a MultiScale AutoEncoder (MSAE) based extreme image codingcompression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the priors at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network (GAN) with multiscale discriminators to perform the endtoend trainable ratedistortion optimization. We compare the perceptual quality of our reconstructions with traditional compression algorithms using HighEfficiency Video Coding (HEVC) based Intra Profile and JPEG2000 on the public Cityscapes, ADE20K and Kodak datasets, demonstrating the significant subjective quality improvement. However, objective measurements, such as PSNR, SSIM, etc, are often deteriorated by applying the generative adversarial optimization.