{"id":1111,"date":"2025-09-24T19:37:49","date_gmt":"2025-09-24T22:37:49","guid":{"rendered":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/?page_id=1111"},"modified":"2025-09-24T19:49:32","modified_gmt":"2025-09-24T22:49:32","slug":"poster-session-i","status":"publish","type":"page","link":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/poster-session-i\/","title":{"rendered":"Poster Session I"},"content":{"rendered":"

Poster Session I \u2013 October 6th, 17h00min \u2013 18h00min (GMT-3)<\/strong><\/span><\/h2>\n

Participants with accepted posters should carefully follow the guidelines below to ensure a smooth and organized presentation during the poster sessions:<\/p>\n

    \n
  • Posters must be printed in A0 format: 84.1 cm wide \u00d7 118.9 cm high (portrait orientation).<\/li>\n
  • Each poster should be equipped with a hanger or other appropriate mechanism to allow secure and visible mounting on the display panels provided.<\/li>\n
  • Poster sessions will take place in the entrance hall of the main auditorium at the event venue.<\/li>\n
  • Every poster board will be labeled with the corresponding poster ID and title to help authors locate their assigned space.<\/li>\n
  • Posters should be placed in their designated locations at the beginning of the day of the assigned session. This allows attendees to view the posters freely throughout the day, even before the official poster session begins.<\/li>\n<\/ul>\n

     <\/p>\n

    ID 3 – Employing Approximate Storage for Rate-Distortion Optimization in VVC Encoders<\/strong><\/p>\n

    Matheus Isquierdo (Federal University of Pelotas)*; Felipe Sampaio (Federal Institute of Rio Grande do Sul); Bruno Zatt (Federal University of Pelotas); Nikil Dutt (University of California, Irvine); Daniel Palomino (Federal University of Pelotas)<\/em><\/p>\n

     <\/p>\n

    ID 5 – Fast Complexity Prediction for Test Zone Search Algorithm in VVC<\/strong><\/p>\n

    Vinicius Reis (UFPel)*; Matheus Isquierdo (UFPel); Bruno Zatt (UFPel); Daniel Palomino (UFPel)<\/em><\/p>\n

     <\/p>\n

    ID 7 – Automatic Flow Regime Identification from High-Resolution Two-Phase Flow Images<\/strong><\/p>\n

    Nat\u00e1ssia\u00a0 Siqueira (UFPel)*; Jo\u00e3o In\u00e1cio\u00a0 Moreira Bezerra (UFPel); Oscar Mauricio\u00a0 Hernandez Rodriguez (USP); Marlon Maur\u00edcio\u00a0 Hernandez Cely (UFPel); Daniel Munari Vilchez Palomino (UFPel)<\/em><\/p>\n

     <\/p>\n

    ID 8 – Dynamic Adoption of Decoupled Interpolated Video Coding<\/strong><\/p>\n

    Gabriela Furtado da Silveira (Universidade Federal de Santa Catarina)*; Andr\u00e9 Beims Br\u00e4scher (Universidade Federal de Santa Catarina); Ismael Seidel (Universidade Federal de Santa Catarina); Jos\u00e9 Lu\u00eds Almada G\u00fcntzel (Universidade Federal de Santa Catarina)<\/em><\/p>\n

     <\/p>\n

    ID 10 – Fast Interpolation Filter Decision using Machine Learning for the Fractional Inter-Prediction of AV1<\/strong><\/p>\n

    Patrick Rosa (Universidade Federal de Pelotas)*; Daiane Freitas (Universidade Federal de Pelotas); Leonardo M\u00fcller (Universidade Federal de Pelotas); Guilherme Corr\u00eaa (Universidade Federal de Pelotas); Daniel Palomino (Universidade Federal de Pelotas)<\/em><\/p>\n

     <\/p>\n

    ID 18 – Machine Learning-Based Affine Motion Estimation to Reduce VVC Encoding Time<\/strong><\/p>\n

    Ramiro Viana (UFPel – Federal University of Pelotas)*; Marcelo Porto (UFPel – Federal University of Pelotas); Guilherme Corr\u00eaa (UFPel – Federal University of Pelotas); Luciano Agostini (UFPel – Federal University of Pelotas)<\/em><\/p>\n

     <\/p>\n

    ID 24 – A Multi-level Methodology for the Massive Acceleration of Affine Motion Estimation on VVC using GPUs<\/strong><\/p>\n

    Iago Storch (Federal University of Pelotas)*; Sergio Bampi (UFRGS); Daniel Palomino (UFPel)<\/em><\/p>\n

     <\/p>\n

    ID 29 – Dynamic Approximate Storage Scheme for VVC Intra-Frame Prediction<\/strong><\/p>\n

    Yasmin Camargo (Federal University of Pelotas)*; Felipe Sampaio (IFRS); Daniel Palomino (Federal University of Pelotas); Bruno Zatt (Federal University of Pelotas); Matheus Isquierdo (Federal University of Pelotas)<\/em><\/p>\n

     <\/p>\n

    ID 30 – Learning-Based Operand Isolation for Power-Aware Hardware Design of AV1 FME\/MC Interpolation<\/strong><\/p>\n

    Leonardo M\u00fcller (Federal University of Pelotas)*; Daiane Freitas (Federal University of Pelotas); Patrick Rosa (Federal University of Pelotas); Cl\u00e1udio Diniz (Federal University of Rio Grande do Sul); Mateus Grellert (Federal University of Rio Grande do Sul); Guilherme Correa (Federal University of Pelotas)<\/em><\/p>\n

     <\/p>\n

    ID 32 – Inter-Frame Prediction Analysis Using VVenC Across Coding Profiles<\/strong><\/p>\n

    Rodrigo Feldens (Federal University of Rio Grande do Sul (UFRGS))*; Sergio Bampi (Federal University of Rio Grande do Sul (UFRGS)); Felipe Sampaio (Federal Institute of Rio Grande do Sul (IFRS))<\/em><\/p>\n

     <\/p>\n

    ID 34 – Performance and Energy Consumption Evaluation of the VVC ALF Classification Step Across Different Programming Paradigms and Hardware Platforms<\/strong><\/p>\n

    Vit\u00f3ria Fabricio (Video Technology Research Group, Federal University of Pelotas)*; Iago\u00a0 Storch (Video Technology Research Group, Federal University of Pelotas); Guilherme Correa (Video Technology Research Group, Federal University of Pelotas); Daniel Palomino (Video Technology Research Group, Federal University of Pelotas)”<\/em><\/p>\n

     <\/p>\n

    ID 35 – A Hardware Architecture for Smoothing Filters in H.266\/VVC Intra-Prediction<\/strong><\/p>\n

    Sara Henssler (Universidade Federal do Pampa – UNIPAMPA)*; Luciano Volcan Agostini (Universidade Federal de Pelotas (UFPel)); Marcel Moscarelli Corr\u00eaa (Instituto Federal Sul-rio-grandense (IFSul))<\/em><\/p>\n

     <\/p>\n

    ID 41 – Machine Learning-Accelerated VVC for 360\u00b0 Video Intra-Prediction<\/strong><\/p>\n

    FRANKLIN DE OLIVEIRA (Universidade Federal de Pelotas)*; LUCAS DA SILVA (Universidade Federal de Pelotas); IAGO STORCH (Universidade Federal de Pelotas); LUCIANO AGOSTINI (Universidade Federal de Pelotas)<\/em><\/p>\n

     <\/p>\n

    ID 42 – Intra-frame prediction correlation analysis over Random Access frame levels<\/strong><\/p>\n

    Laiane Souza (Federal University of Pelotas)*, Felipe Sampaio (IFRS)<\/em><\/p>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

    Poster Session I \u2013 October 6th, 17h00min \u2013 18h00min (GMT-3) Participants with accepted posters should carefully follow the guidelines below to ensure a smooth and organized presentation during the poster sessions: Posters must be...<\/p>\n","protected":false},"author":746,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1111","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/pages\/1111","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/users\/746"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/comments?post=1111"}],"version-history":[{"count":3,"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/pages\/1111\/revisions"}],"predecessor-version":[{"id":1125,"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/pages\/1111\/revisions\/1125"}],"wp:attachment":[{"href":"https:\/\/wp.ufpel.edu.br\/dpvsa2025\/wp-json\/wp\/v2\/media?parent=1111"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}