{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/blockcnn-a-deep-network-for-artifact-removal","title":"BlockCNN: A Deep Network for Artifact Removal and Image Compression","arxiv_id":"1805.11091","date":"2018-05-28","proceeding":null,"authors":["Danial Maleki","Soheila Nadalian","Mohammad Mahdi Derakhshani","Mohammad Amin Sadeghi"],"abstract":"We present a general technique that performs both artifact removal and image\ncompression. For artifact removal, we input a JPEG image and try to remove its\ncompression artifacts. For compression, we input an image and process its 8 by\n8 blocks in a sequence. For each block, we first try to predict its intensities\nbased on previous blocks; then, we store a residual with respect to the input\nimage. Our technique reuses JPEG's legacy compression and decompression\nroutines. Both our artifact removal and our image compression techniques use\nthe same deep network, but with different training weights. Our technique is\nsimple and fast and it significantly improves the performance of artifact\nremoval and image compression.","url_abs":"http://arxiv.org/abs/1805.11091v1","url_pdf":"http://arxiv.org/pdf/1805.11091v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"blockcnn-a-deep-network-for-artifact-removal","repo_url":"https://github.com/DaniMlk/BlockCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-compression","task_name":"Image Compression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}