Codedretrieval: Joint Image Compression and Retrieval with Neural Networks With the explosive increase of image data, the efficiency of both image compression and retrieval becomes unprecedentedly significant. However, these two tasks are usually isolated executed, which waste great computational resources in largescale image applications. In this work, we propose a joint framework called CodedRetrieval, which can find a general feature expression for both compression and retrieval based on neural network. Additionally, a two stage training strategy is designed to achieve better balance between the two distinct tasks. Experimental results show that our method can achieve competitive perfomance on both compression and retrieval comparing to classic methods, while saving great amount of computation time.

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.