Compressive Synthetic Aperture Radar Imaging and Autofocusing By Augmented Lagrangian Methods

Domain adaptation is an efficient technique to improve the performance of a system by adapting a pre-trained model to the given input data. The adaptation technique has been generally applied in conventional video codecs. For neural network-based systems, the encoder may adapt the decoder to the input data by fine-tuning a pre-trained model present at the decoder side. The weight update is then transferred to the decoder and the updated model is used to decode the bitstream. However, due to the large number of parameters in deep neural networks, the overhead of the weight update may diminish the gain from the adaptation technique. in recent years, various methods have been proposed to reduce the overhead without significantly compromising the gain. in this paper, we propose an adaptive multi-scale progressive probability model for lossless image compression. The proposed method uses the data that has already been processed at the inference stage to fine-tune the probability model. Importantly, the decoder can apply the fine-tuning by itself resulting a small adaptation overhead to help the decoder in performing the fine-tuning. The proposed method achieves up to 0.28 bits-per-pixel (BPP) reduction on four benchmark datasets compared to the state-of-the-art method.