{"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/mfqe-20-a-new-approach-for-multi-frame","title":"MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video","arxiv_id":"1902.09707","date":"2019-02-26","proceeding":null,"authors":["Qunliang Xing","Zhenyu Guan","Mai Xu","Ren Yang","Tie Liu","Zulin Wang"],"abstract":"The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we firstly develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code is available at https://github.com/RyanXingQL/MFQEv2.0.git.","url_abs":"https://arxiv.org/abs/1902.09707v6","url_pdf":"https://arxiv.org/pdf/1902.09707v6.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":"mfqe-20-a-new-approach-for-multi-frame","repo_url":"https://github.com/RyanXingQL/MFQEv2.0","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"video-enhancement","task_name":"Video Enhancement"},{"task_slug":"video-restoration","task_name":"Video Restoration"}],"methods":[],"datasets_introduced":[{"slug":"mfqe-v2","name":"MFQE v2","full_name":"Multi-Frame Quality Enhancement v2 Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-enhancement-on-mfqe-v2","task":"Video Enhancement","dataset":"MFQE v2","model":"MFQE 2.0","rank_in_archive_order":5,"of":6,"metrics":{"Incremental PSNR":"0.56","Parameters(M)":"0.25"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.09707","atlas_url":"https://app.syntology.ai/?focus=1902.09707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09707"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/RyanXingQL/MFQEv2.0","reach":null}],"summary":{"ran_fixture":2,"ran_honours":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"082b88b38bb2f324","entry":"return_NPIndices","repo":"RyanXingQL/MFQEv2.0","repo_kind":"official","path":"main_test.py","file_url":"https://github.com/RyanXingQL/MFQEv2.0/blob/HEAD/main_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"082b88b38bb2f324"}},{"code_sha256_prefix":"565679da17f50d2c","entry":"return_PQFIndices","repo":"RyanXingQL/MFQEv2.0","repo_kind":"official","path":"main_test.py","file_url":"https://github.com/RyanXingQL/MFQEv2.0/blob/HEAD/main_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"565679da17f50d2c"}},{"code_sha256_prefix":"517c29289555a58d","entry":"y_import","repo":"RyanXingQL/MFQEv2.0","repo_kind":"official","path":"main_test.py","file_url":"https://github.com/RyanXingQL/MFQEv2.0/blob/HEAD/main_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"517c29289555a58d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}