Color Image Compression with Transform Domain DownSampling and Deep Convolutional Reconstruction In this paper, we build up a new blockbased color image compression scheme based on our proposed transform domain downsampling method and deep convolutional reconstruction algorithm. Specifically, our proposed downsampling scheme aims to downsample each N xc3x97 N transform block into the N2 xc3x97 N2 block for the saving of bitcost. On the other hand, the proposed deep convolutional reconstruction algorithm is employed to reconstruct the downsampled block for a full resolution reconstruction. We apply our proposed methods to both the chrominance components to compress color images. Experimental results show that our proposed method achieves excellent results when used in practice.

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.