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Existing algorithms either\nfocus on removing blocking artifacts and produce blurred output, or restores\nsharpened images that are accompanied with ringing effects. Inspired by the\ndeep convolutional networks (DCN) on super-resolution, we formulate a compact\nand efficient network for seamless attenuation of different compression\nartifacts. We also demonstrate that a deeper model can be effectively trained\nwith the features learned in a shallow network. Following a similar \"easy to\nhard\" idea, we systematically investigate several practical transfer settings\nand show the effectiveness of transfer learning in low-level vision problems.\nOur method shows superior performance than the state-of-the-arts both on the\nbenchmark datasets and the real-world use case (i.e. Twitter). In addition, we\nshow that our method can be applied as pre-processing to facilitate other\nlow-level vision routines when they take compressed images as input.","url_abs":"http://arxiv.org/abs/1504.06993v1","url_pdf":"http://arxiv.org/pdf/1504.06993v1.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":"compression-artifacts-reduction-by-a-deep","repo_url":"https://github.com/ankitf/artifact_reduction_jpeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"compression-artifacts-reduction-by-a-deep","repo_url":"https://github.com/ryanxingql/powerqe","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"compression-artifacts-reduction-by-a-deep","repo_url":"https://github.com/volvet/ARCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"compression-artifacts-reduction-by-a-deep","repo_url":"https://github.com/vinayak19th/ARCNN-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"blocking","task_name":"Blocking"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"jpeg-artifact-correction","task_name":"JPEG Artifact Correction"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/jpeg-artifact-correction-on-icb-quality-10","task":"JPEG Artifact Correction","dataset":"ICB (Quality 10 Color)","model":"ARCNN","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"30.06","PSNR-B":"31.21","SSIM":"0.779"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-icb-quality-10-1","task":"JPEG Artifact Correction","dataset":"ICB (Quality 10 Grayscale)","model":"ARCNN","rank_in_archive_order":5,"of":5,"metrics":{"PSNR":"31.13","PSNR-B":"30.97","SSIM":"0.794"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-icb-quality-20","task":"JPEG Artifact Correction","dataset":"ICB (Quality 20 Color)","model":"ARCNN","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"32.24","PSNR-B":"32.53","SSIM":"0.778"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-icb-quality-20-1","task":"JPEG Artifact Correction","dataset":"ICB (Quality 20 Grayscale)","model":"ARCNN","rank_in_archive_order":4,"of":5,"metrics":{"PSNR":"35.04","PSNR-B":"32.72","SSIM":"0.905"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-icb-quality-30","task":"JPEG Artifact Correction","dataset":"ICB (Quality 30 Color)","model":"ARCNN","rank_in_archive_order":4,"of":4,"metrics":{"PSNR":"33.31","PSNR-B":"33.72","SSIM":"0.807"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-10","task":"JPEG Artifact Correction","dataset":"LIVE1 (Quality 10 Color)","model":"ARCNN","rank_in_archive_order":9,"of":9,"metrics":{"PSNR":"26.91","PSNR-B":"26.92","SSIM":"0.795"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-20","task":"JPEG Artifact Correction","dataset":"LIVE1 (Quality 20 Color)","model":"ARCNN","rank_in_archive_order":9,"of":9,"metrics":{"PSNR":"29.23","PSNR-B":"29.24","SSIM":"0.865"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-20-1","task":"JPEG Artifact Correction","dataset":"LIVE1 (Quality 20 Grayscale)","model":"ARCNN","rank_in_archive_order":12,"of":12,"metrics":{"PSNR":"31.29","PSNR-B":"31.37","SSIM":"0.8891"},"uses_additional_data":false},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-10-1","task":"JPEG Artifact Correction","dataset":"Live1 (Quality 10 Grayscale)","model":"ARCNN","rank_in_archive_order":12,"of":13,"metrics":{"PSNR":"29.11","PSNR-B":"29.07","SSIM":"0.8235"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1504.06993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.06993"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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