{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:10:40Z","timestamp":1753884640215,"version":"3.41.2"},"reference-count":46,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s021946782350047x","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T05:52:56Z","timestamp":1669269176000},"source":"Crossref","is-referenced-by-count":0,"title":["BCS-AE: Integrated Image Compression-Encryption Model Based on AE and Block-CS"],"prefix":"10.1142","volume":"23","author":[{"given":"Samer Kais","family":"Jameel","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Raparin, Rania, Iraq"}]},{"given":"Jafar","family":"Majidpour","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Raparin, Rania, Iraq"}]}],"member":"219","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"volume-title":"Proc. Int. Conf. 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