Poster Session I – October 6th, 17h00min – 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 printed in A0 format: 84.1 cm wide × 118.9 cm high (portrait orientation).
- Each poster should be equipped with a hanger or other appropriate mechanism to allow secure and visible mounting on the display panels provided.
- Poster sessions will take place in the entrance hall of the main auditorium at the event venue.
- Every poster board will be labeled with the corresponding poster ID and title to help authors locate their assigned space.
- 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.
ID 3 – Employing Approximate Storage for Rate-Distortion Optimization in VVC Encoders
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)
ID 5 – Fast Complexity Prediction for Test Zone Search Algorithm in VVC
Vinicius Reis (UFPel)*; Matheus Isquierdo (UFPel); Bruno Zatt (UFPel); Daniel Palomino (UFPel)
ID 7 – Automatic Flow Regime Identification from High-Resolution Two-Phase Flow Images
Natássia Siqueira (UFPel)*; João Inácio Moreira Bezerra (UFPel); Oscar Mauricio Hernandez Rodriguez (USP); Marlon Maurício Hernandez Cely (UFPel); Daniel Munari Vilchez Palomino (UFPel)
ID 8 – Dynamic Adoption of Decoupled Interpolated Video Coding
Gabriela Furtado da Silveira (Universidade Federal de Santa Catarina)*; André Beims Bräscher (Universidade Federal de Santa Catarina); Ismael Seidel (Universidade Federal de Santa Catarina); José Luís Almada Güntzel (Universidade Federal de Santa Catarina)
ID 10 – Fast Interpolation Filter Decision using Machine Learning for the Fractional Inter-Prediction of AV1
Patrick Rosa (Universidade Federal de Pelotas)*; Daiane Freitas (Universidade Federal de Pelotas); Leonardo Müller (Universidade Federal de Pelotas); Guilherme Corrêa (Universidade Federal de Pelotas); Daniel Palomino (Universidade Federal de Pelotas)
ID 18 – Machine Learning-Based Affine Motion Estimation to Reduce VVC Encoding Time
Ramiro Viana (UFPel – Federal University of Pelotas)*; Marcelo Porto (UFPel – Federal University of Pelotas); Guilherme Corrêa (UFPel – Federal University of Pelotas); Luciano Agostini (UFPel – Federal University of Pelotas)
ID 24 – A Multi-level Methodology for the Massive Acceleration of Affine Motion Estimation on VVC using GPUs
Iago Storch (Federal University of Pelotas)*; Sergio Bampi (UFRGS); Daniel Palomino (UFPel)
ID 29 – Dynamic Approximate Storage Scheme for VVC Intra-Frame Prediction
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)
ID 30 – Learning-Based Operand Isolation for Power-Aware Hardware Design of AV1 FME/MC Interpolation
Leonardo Müller (Federal University of Pelotas)*; Daiane Freitas (Federal University of Pelotas); Patrick Rosa (Federal University of Pelotas); Cláudio Diniz (Federal University of Rio Grande do Sul); Mateus Grellert (Federal University of Rio Grande do Sul); Guilherme Correa (Federal University of Pelotas)
ID 32 – Inter-Frame Prediction Analysis Using VVenC Across Coding Profiles
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))
ID 34 – Performance and Energy Consumption Evaluation of the VVC ALF Classification Step Across Different Programming Paradigms and Hardware Platforms
Vitória Fabricio (Video Technology Research Group, Federal University of Pelotas)*; Iago 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)”
ID 35 – A Hardware Architecture for Smoothing Filters in H.266/VVC Intra-Prediction
Sara Henssler (Universidade Federal do Pampa – UNIPAMPA)*; Luciano Volcan Agostini (Universidade Federal de Pelotas (UFPel)); Marcel Moscarelli Corrêa (Instituto Federal Sul-rio-grandense (IFSul))
ID 41 – Machine Learning-Accelerated VVC for 360° Video Intra-Prediction
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)
ID 42 – Intra-frame prediction correlation analysis over Random Access frame levels
Laiane Souza (Federal University of Pelotas)*, Felipe Sampaio (IFRS)