{"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/cas-cnn-a-deep-convolutional-neural-network","title":"CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression","arxiv_id":"1611.07233","date":"2016-11-22","proceeding":null,"authors":["Lukas Cavigelli","Pascal Hager","Luca Benini"],"abstract":"Lossy image compression algorithms are pervasively used to reduce the size of\nimages transmitted over the web and recorded on data storage media. However, we\npay for their high compression rate with visual artifacts degrading the user\nexperience. Deep convolutional neural networks have become a widespread tool to\naddress high-level computer vision tasks very successfully. Recently, they have\nfound their way into the areas of low-level computer vision and image\nprocessing to solve regression problems mostly with relatively shallow\nnetworks.\n  We present a novel 12-layer deep convolutional network for image compression\nartifact suppression with hierarchical skip connections and a multi-scale loss\nfunction. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an\nimprovement of up to 0.36 dB over the best previous ConvNet result. We show\nthat a network trained for a specific quality factor (QF) is resilient to the\nQF used to compress the input image - a single network trained for QF 60\nprovides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.","url_abs":"http://arxiv.org/abs/1611.07233v1","url_pdf":"http://arxiv.org/pdf/1611.07233v1.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":"cas-cnn-a-deep-convolutional-neural-network","repo_url":"https://github.com/ShakedDovrat/JpegArtifactRemoval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-compression","task_name":"Image Compression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.07233","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}