H.265/HEVC Decoding via Iterative Recovery From Incomplete Quantized Measurements
This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal recovery from incomplete measurements taken in the HEVC transform domain. The recovery could be obtained via <inline-formula><tex-math...
Gespeichert in:
| Veröffentlicht in: | IEEE signal processing letters Jg. 32; S. 4149 - 4153 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1070-9908, 1558-2361 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal recovery from incomplete measurements taken in the HEVC transform domain. The recovery could be obtained via <inline-formula><tex-math notation="LaTeX">l_{1}</tex-math></inline-formula>-minimization using the Iterative Shrinkage-Thresholding Algorithm (ISTA) well known in compressive sensing (CS) framework. However, in case of HEVC the ISTA updating step cannot be performed directly via matrix multiplications, since the sensing is performed using high-complex hybrid intra- and motion-compensated prediction in pixel domain, and the frame sensing matrix depends on the current and corresponding reference frames along with the encoder compression profile. In order to overcome these limitations, in this letter, we first propose to modify the HEVC decoder so that it also obtains the prediction values for each pixel taking into account all the coding modes within the input bitstream. Second, we propose to modify the ISTA updating step by introducing encoding and decoding operators applied instead of the matrix multiplication by sensing matrix and its transpose, respectively. These operators use the obtained prediction values, as well as prediction mode, motion vectors, quantization step, and transform type extracted for each coding unit from the input bitstream in order to replicate the encoding and the decoding process except the entropy coding. Herewith, the ISTA thresholding stage is performed by an image or video enhancement neural network. Experimental results show that the proposed approach provides up to 1 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to the state-of-the-art approaches such as Recursive Fusion and Deformable Spatiotemporal Attention (RFDA), Spatio-Temporal Detail Information Retrieval (STDR) and Coding Priors-Guided Aggregation (CPGA). |
|---|---|
| AbstractList | This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal recovery from incomplete measurements taken in the HEVC transform domain. The recovery could be obtained via [Formula Omitted]-minimization using the Iterative Shrinkage-Thresholding Algorithm (ISTA) well known in compressive sensing (CS) framework. However, in case of HEVC the ISTA updating step cannot be performed directly via matrix multiplications, since the sensing is performed using high-complex hybrid intra- and motion-compensated prediction in pixel domain, and the frame sensing matrix depends on the current and corresponding reference frames along with the encoder compression profile. In order to overcome these limitations, in this letter, we first propose to modify the HEVC decoder so that it also obtains the prediction values for each pixel taking into account all the coding modes within the input bitstream. Second, we propose to modify the ISTA updating step by introducing encoding and decoding operators applied instead of the matrix multiplication by sensing matrix and its transpose, respectively. These operators use the obtained prediction values, as well as prediction mode, motion vectors, quantization step, and transform type extracted for each coding unit from the input bitstream in order to replicate the encoding and the decoding process except the entropy coding. Herewith, the ISTA thresholding stage is performed by an image or video enhancement neural network. Experimental results show that the proposed approach provides up to 1 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to the state-of-the-art approaches such as Recursive Fusion and Deformable Spatiotemporal Attention (RFDA), Spatio-Temporal Detail Information Retrieval (STDR) and Coding Priors-Guided Aggregation (CPGA). This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal recovery from incomplete measurements taken in the HEVC transform domain. The recovery could be obtained via <inline-formula><tex-math notation="LaTeX">l_{1}</tex-math></inline-formula>-minimization using the Iterative Shrinkage-Thresholding Algorithm (ISTA) well known in compressive sensing (CS) framework. However, in case of HEVC the ISTA updating step cannot be performed directly via matrix multiplications, since the sensing is performed using high-complex hybrid intra- and motion-compensated prediction in pixel domain, and the frame sensing matrix depends on the current and corresponding reference frames along with the encoder compression profile. In order to overcome these limitations, in this letter, we first propose to modify the HEVC decoder so that it also obtains the prediction values for each pixel taking into account all the coding modes within the input bitstream. Second, we propose to modify the ISTA updating step by introducing encoding and decoding operators applied instead of the matrix multiplication by sensing matrix and its transpose, respectively. These operators use the obtained prediction values, as well as prediction mode, motion vectors, quantization step, and transform type extracted for each coding unit from the input bitstream in order to replicate the encoding and the decoding process except the entropy coding. Herewith, the ISTA thresholding stage is performed by an image or video enhancement neural network. Experimental results show that the proposed approach provides up to 1 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to the state-of-the-art approaches such as Recursive Fusion and Deformable Spatiotemporal Attention (RFDA), Spatio-Temporal Detail Information Retrieval (STDR) and Coding Priors-Guided Aggregation (CPGA). |
| Author | Mahfod, Karam Belyaev, Evgeny |
| Author_xml | – sequence: 1 givenname: Karam orcidid: 0009-0002-0660-9434 surname: Mahfod fullname: Mahfod, Karam email: karammahfod@itmo.ru organization: ITMO University, St. Petersburg, Russia – sequence: 2 givenname: Evgeny orcidid: 0000-0003-1245-0140 surname: Belyaev fullname: Belyaev, Evgeny organization: ITMO University, St. Petersburg, Russia |
| BookMark | eNpFkE1Lw0AQhhepYFu9e_Cw4DnpfmdzlNraQkXr13XZZCeS0iR1NynUX29KC57mZXjeGXhGaFA3NSB0S0lMKUknq_fXmBEmY66YUIm-QEMqpY4YV3TQZ5KQKE2JvkKjEDaEEE21HKL1ImZKThazryl-hLxxZf2N96XFyxa8bcs94Ld-vQd_wHPfVHhZ502120ILeN3Zui1_weFnsKHzUEHdhmt0WdhtgJvzHKPP-exjuohWL0_L6cMqyplI2kimmdRMZdox7YhjTmuRu4xZASKTSa6E5TxlyhaO58JqXiSFTXnGeWFpj_Ixuj_d3fnmp4PQmk3T-bp_aThTOmUilbqnyInKfROCh8LsfFlZfzCUmKM404szR3HmLK6v3J0qJQD845RRqTjjf5p5aog |
| CODEN | ISPLEM |
| Cites_doi | 10.1109/MMSP48831.2020.9287147 10.1109/ICIP.2014.7025426 10.1109/TCSVT.2018.2867568 10.1109/LSP.2024.3429008 10.1609/aaai.v34i07.6697 10.1109/DSPA60853.2024.10510112 10.1109/TCSVT.2012.2221191 10.1109/LSP.2024.3407536 10.1049/iet-ipr.2019.0661 10.1109/76.889025 10.1109/CVPR52733.2024.00286 10.3390/s23031368 10.5555/3104322.3104374 10.1109/TMM.2013.2269315 10.1109/LSP.2022.3147441 10.1109/TPAMI.2019.2944806 10.1109/TIT.2006.871582 10.1109/CVPR.2016.302 10.1109/LCOMM.2017.2697428 10.1109/DSPA64310.2025.10977879 10.1109/ICCV48922.2021.00495 10.1007/978-3-319-51811-4_3 10.1109/TCSVT.2012.2221192 10.1109/EUSIPCO.2015.7362905 10.1145/3474085.3475710 10.1109/TIP.2003.819861 10.1109/TMM.2022.3214775 10.1109/TSP.2011.2170977 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/LSP.2025.3624678 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-2361 |
| EndPage | 4153 |
| ExternalDocumentID | 10_1109_LSP_2025_3624678 11215632 |
| Genre | orig-research |
| GroupedDBID | -~X .DC 0R~ 29I 3EH 4.4 5GY 5VS 6IK 85S 97E AAJGR AASAJ AAWTH AAYJJ ABAZT ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TAE TN5 VH1 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c247t-59b5826b8d28d0d2d884cdb2a4e4b57c64a33926afd3c4a83f7fa93b33fa184c3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001608941800017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1070-9908 |
| IngestDate | Thu Nov 06 14:18:39 EST 2025 Sat Nov 29 06:54:40 EST 2025 Wed Nov 19 08:27:08 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c247t-59b5826b8d28d0d2d884cdb2a4e4b57c64a33926afd3c4a83f7fa93b33fa184c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0009-0002-0660-9434 0000-0003-1245-0140 |
| PQID | 3268924958 |
| PQPubID | 75747 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_11215632 proquest_journals_3268924958 crossref_primary_10_1109_LSP_2025_3624678 |
| PublicationCentury | 2000 |
| PublicationDate | 20250000 2025-00-00 20250101 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 20250000 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE signal processing letters |
| PublicationTitleAbbrev | LSP |
| PublicationYear | 2025 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 ref11 ref10 ref2 ref1 ref17 ref16 ref19 ref18 (ref24) 2017 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref15 doi: 10.1109/MMSP48831.2020.9287147 – ident: ref23 doi: 10.1109/ICIP.2014.7025426 – ident: ref6 doi: 10.1109/TCSVT.2018.2867568 – ident: ref8 doi: 10.1109/LSP.2024.3429008 – ident: ref26 doi: 10.1609/aaai.v34i07.6697 – ident: ref22 doi: 10.1109/DSPA60853.2024.10510112 – ident: ref1 doi: 10.1109/TCSVT.2012.2221191 – ident: ref7 doi: 10.1109/LSP.2024.3407536 – ident: ref21 doi: 10.1049/iet-ipr.2019.0661 – ident: ref18 doi: 10.1109/76.889025 – ident: ref9 doi: 10.1109/CVPR52733.2024.00286 – ident: ref13 doi: 10.3390/s23031368 – ident: ref12 doi: 10.5555/3104322.3104374 – ident: ref19 doi: 10.1109/TMM.2013.2269315 – ident: ref27 doi: 10.1109/LSP.2022.3147441 – ident: ref2 doi: 10.1109/TPAMI.2019.2944806 – ident: ref10 doi: 10.1109/TIT.2006.871582 – ident: ref14 doi: 10.1109/CVPR.2016.302 – ident: ref16 doi: 10.1109/LCOMM.2017.2697428 – ident: ref17 doi: 10.1109/DSPA64310.2025.10977879 – ident: ref25 doi: 10.1109/ICCV48922.2021.00495 – ident: ref3 doi: 10.1007/978-3-319-51811-4_3 – ident: ref28 doi: 10.1109/TCSVT.2012.2221192 – ident: ref20 doi: 10.1109/EUSIPCO.2015.7362905 – ident: ref4 doi: 10.1145/3474085.3475710 – ident: ref29 doi: 10.1109/TIP.2003.819861 – year: 2017 ident: ref24 article-title: A fork of libav containing only essential files for decoding HEVC content – ident: ref5 doi: 10.1109/TMM.2022.3214775 – ident: ref11 doi: 10.1109/TSP.2011.2170977 |
| SSID | ssj0008185 |
| Score | 2.4387808 |
| Snippet | This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 4149 |
| SubjectTerms | Binary sequences Coding compressive sensing Decoding Encoding Filtering Formability HEVC Image reconstruction Information retrieval Neural networks Operators (mathematics) Pixels Recovery Sensors Signal reconstruction Signal to noise ratio Silicon Transforms Video compression video enhancement Video sequences Videos |
| Title | H.265/HEVC Decoding via Iterative Recovery From Incomplete Quantized Measurements |
| URI | https://ieeexplore.ieee.org/document/11215632 https://www.proquest.com/docview/3268924958 |
| Volume | 32 |
| WOSCitedRecordID | wos001608941800017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2361 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008185 issn: 1070-9908 databaseCode: RIE dateStart: 19940101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELagYoCBN6JQkAcWhrSJHSf2iEqrIpWqFVB1i_yK1IEW9SXBr-fspBSEGNgyJFb0-fzdfWffGaEb4mtkhAwshL8BGIVLNEkb2DSC9QEegfm-BcNu2uvx0Uj0y2J1XwtjrfWHz2zdPfq9fDPVS5cqa0SuFUJCgXG30zQpirW-aNd5nuKAYRgAxfL1nmQoGt2nPihBwurA1kAM_IcP8peq_GJi717aB__8sUO0X8aR-K6Y-CO0ZSfHaO9bd8ETNOjUScIandawie9BZTovhVdjiR98J2WgOezEJ9jyO27Ppq8YuMIdMIcwGg-WgPj4wxr8uEkizk_RS7v13OwE5Q0KgSZxugiYUAz0g-KGcBMaYjiPtVFExjZWLNVJLCkESInMDdWx5DRPcymoojSXIP00PUOVyXRizxGWwkQqJrlxDfKF5opZLWKRR4IbqqSsots1ptlb0Sgj8wIjFBngnzn8sxL_Kjp1GG7eK-Grotp6FrJyKc0ziC-5E4mMX_zx2SXadaMXiZEaqixmS3uFdvRqMZ7Prr2VfAJGvLiu |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwELVQQQIO7Iiy-sCFQ9rES2MfEVAFEapWLOotcmxH6oEWdZPg6xk7KYsQB245JHL0PH4zb-wZI3ROfI2MVIGF8DcAo3CJJmUDG0ewPsAjcN-34DmNOx3R78tuVazua2Gstf7wmW24R7-Xb0Z65lJlzci1QmhRYNxlzhgJy3KtT-J1vqc8YhgGQLJisSsZymb60AUtSHgD-BqoQfzwQv5alV9c7B1Me_Ofv7aFNqpIEl-WU7-NluxwB61_6y-4i3pJg7R4M7l5vsLXoDOdn8LzgcK3vpcyEB128hOs-Q23x6MXDGzhjphDII17M8B88G4Nvv9KI0720FP75vEqCao7FAJNWDwNuMw5KIhcGCJMaIgRgmmTE8Usy3msW0xRCJFaqjBUMyVoERdK0pzSQoH403Qf1YajoT1AWEkT5YwUxrXIl1rk3GrJZBFJYWiuVB1dLDDNXstWGZmXGKHMAP_M4Z9V-NfRnsPw670Kvjo6XsxCVi2mSQYRpnAykYvDPz47Q6vJ432apbeduyO05kYq0yTHqDYdz-wJWtHz6WAyPvUW8wEPTLv1 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=H.265%2FHEVC+Decoding+via+Iterative+Recovery+From+Incomplete+Quantized+Measurements&rft.jtitle=IEEE+signal+processing+letters&rft.au=Mahfod%2C+Karam&rft.au=Belyaev%2C+Evgeny&rft.date=2025&rft.pub=IEEE&rft.issn=1070-9908&rft.volume=32&rft.spage=4149&rft.epage=4153&rft_id=info:doi/10.1109%2FLSP.2025.3624678&rft.externalDocID=11215632 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1070-9908&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1070-9908&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1070-9908&client=summon |