| Abstrakt: |
For Compressive Sensing problems, a number of techniques have been introduced, including traditional compressed-sensing (CS) image reconstruction and Deep Neural Network (DNN) models. Unfortunately, due to low sampling rates, the quality of image reconstruction is still poor. This paper proposes a lossy image compression model (i.e. BCS-AE), which combines two different types to produce a model that uses more high-quality low-bitrate CS reconstruction. Initially, block-based compressed sensing (BCS) was utilized, and it was done one block at a time by the same operator. It can correctly extract images with complex geometric configurations. Second, we create an AutoEncoder architecture to replace traditional transforms, and we train it with a rate-distortion loss function. The proposed model is trained and then tested on the CelebA and Kodak databases. According to the results, advanced deep learning-based and iterative optimization-based algorithms perform better in terms of compression ratio and reconstruction quality. [ABSTRACT FROM AUTHOR] |
