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Efficiency and complexity analysis of video-based and geometry-based point cloud encoders

Journal of Real-Time Image Processing 2025年第3期22卷 1-14页

作者： Santos, Cristiano Rehbein, Gustavo Costa, Eduardo Corrêa, Guilherme Porto, Marcelo Video Technology Research Group Graduate Program in Computing Universidade Federal de Pelotas RS Pelotas Brazil

3D point clouds are increasingly used in applications, such as 3D remapping, cultural heritage preservation, and virtual/augmented reality. Given their large data volume, efficient compression is crucial. The MPEG group has introduced two standards: Geometry-based Point Cloud Compression (G-PCC), for static and dynamic-acquired point clouds, and video-based Point Cloud Compression (V-PCC), specifically for dynamic point clouds. Although both approaches achieve effective data reduction, their high computational complexity limits real-time use, especially on resource-constrained devices. This paper analyzes the computational cost and coding efficiency of G-PCC and V-PCC, identifying the most time-consuming steps. In G-PCC, the Octree-RAHT configuration offers the best trade-off between efficiency and encoding time, with recoloring alone accounting for up to 70%. In V-PCC, video encoding dominates, consuming about 92% of the total time. These findings lay the groundwork for future optimizations to reduce the complexity of more efficient implementations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Motion Picture Experts group standards

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video Quality Enhancement Using Multi-Domain Spatio-Temporal Deformable Fusion 16

Video Quality Enhancement Using Multi-Domain Spatio-Temporal...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Kreisler, Gilberto Da Silveira Junior, Garibaldi Zatt, Bruno Palomino, Daniel Correa, Guilherme Video Technology Research Group - ViTech Graduate Program in Computing - UFPel Pelotas Brazil

ISBN: (纸本)9798331522124

Lossy video compression introduces visual artifacts that degrade video quality, where deep neural networks (DNNs) are effective in enhancement. However, conventional DNN-based methods often focus on a single video compression standard, limiting their deployment in multiple cases. To overcome this issue, this study introduces a multi-domain video quality enhancement architecture based on the Spatio-Temporal Deformable Fusion (STDF) technique. This method enables the model to enhance videos compressed with multiple codecs, maintaining reliable performance across standards. After trained, the proposed architecture was tested with videos compressed by the High Efficiency video Coding (HEVC) encoder, the Versatile video Coding (VVC) encoder, the VP9 codec and the AOMedia video 1 (AV1) codec. Results show an average Peak Signal-to-Noise Ratio (PSNR) improvement between 0.228 dB and 0.787 dB. © 2025 IEEE.

关键词： video streaming

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Computational Cost Analysis and Reduction of VVC Multiple Transform Selection 16

Computational Cost Analysis and Reduction of VVC Multiple Tr...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Camargo, Caroline Silveira, Bianca Correa, Guilherme Federal University of Pelotas - UFPel Video Technology Research Group - ViTech Graduate Program in Computing - Ppgc Pelotas Brazil

ISBN: (纸本)9798331522124

The growing demand for high-definition online videos emphasizes the need for efficient video codecs like H.266/VVC, which offer significant compression potential. However, its implementation presents challenges, particularly in terms of computational cost, as is the case of the Multiple Transform Selection (MTS) tool. This study analyzes the performance of MTS modes, showing that explicit MTS improves coding efficiency but increases encoding time, while implicit MTS offers modest efficiency gains with less computational cost. A machine learning-based approach is proposed, using decision trees, to accelerate encoder decisions for both intra and inter predicted blocks in explicit MTS, reducing encoding time by an average of 7.98%, with only a 0.89% increase in BD-rate. These results highlight the potential for optimization of explicit MTS in both intra and inter transformations. © 2025 IEEE.

关键词： Multiresolution analysis

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video Quality Enhancement Using Multi-Domain Spatio-Temporal Deformable Fusion

Video Quality Enhancement Using Multi-Domain Spatio-Temporal...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Gilberto Kreisler Garibaldi da Silveira Júnior Bruno Zatt Daniel Palomino Guilherme Correa Video Technology Research Group - ViTech Graduate Program in Computing - UFPel Pelotas Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

关键词： Training Visualization Codecs PSNR Artificial neural networks video compression Quality assessment video recording Standards High efficiency video coding

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An Efficient Hardware Design for the VVC Prediction Refinement with Optical Flow

IEEE Design and Test

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IEEE Design and Test 2025年

作者： Perleberg, Murilo Munoz, Marcello Maass, Denis Afonso, Vladimir Agostini, Luciano Porto, Marcelo Federal University of Pelotas Video Technology Research Group - Graduate Program in Computing Pelotas Brazil Video Technology Research Group - Sul-rio-grandense Federal Institute Pelotas Brazil

The Prediction Refinement with Optical Flow (PROF) is incorporated in the Affine Prediction of the Versatile video Coding (VVC) standard. While the Affine Prediction allows the representation of complex motions in the scene, the PROF enhances video encoding efficiency by generating a virtual rotation on each subblock of the current block. Due to the intensive computational processing, this work presents a dedicated hardware design for the PROF and its integration with the Affine Motion Estimation Reconstructor. The synthesis results show that the proposed PROF Core design can process 120 frames per second of 4K Ultra-High-Definition resolution videos while dissipating 2.2mW. © 2013 IEEE.

关键词： Image coding

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A Coding Efficiency-Aware Hardware Design for VVC Affine Motion Estimation Reconstructor

IEEE Design and Test

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IEEE Design and Test 2025年

作者： Muñoz, Marcello M. Maass, Denis Perleberg, Murilo Agostini, Luciano Corrêa, Guilherme Porto, Marcelo Graduate Program in Computing - PPGC Video Technology Research Group ViTech Federal University of Pelotas - UFPel Pelotas Brazil

One of the most important new tools of the Versatile video Coding (VVC) standard is the Affine Motion Estimation (AME). The AME contribution to the coding efficiency comes with a high computational cost, especially for real-time high-resolution video encoding, making dedicated hardware for battery-powered devices mandatory. Considering this scenario, this work presents a dedicated hardware design for the Affine Reconstructor of the VVC standard, focusing on high coding efficiency for real-time processing of UHD video. ASIC synthesis results show a circuit area of 179.9k Gates, with a power dissipation of 107.76 mW when targeting the processing of 4K UHD@60fps videos. The proposed design shows the lowest impact in BD-Rate (only 0.54%) in the current literature. © 2013 IEEE.

关键词： Motion estimation

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Computational Cost Analysis and Reduction of VVC Multiple Transform Selection

Computational Cost Analysis and Reduction of VVC Multiple Tr...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Caroline Camargo Bianca Silveira Guilherme Correa Video Technology Research Group - ViTech Graduate Program in Computing - PPGC Federal University of Pelotas - UFPel Pelotas Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

关键词： Circuits and systems Transforms Machine learning Encoding Computational efficiency Decision trees video codecs Optimization videos

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Evaluation of Coarse-to-Fine Spatio-Temporal Information Fusion (CF-STIF) Network

Evaluation of Coarse-to-Fine Spatio-Temporal Information Fus...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Luis Carlos Linares Gilberto Kreisler Daniel Palomino Guilherme Corrêa Bruno Zatt Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper presents an evaluation of the Coarse-to-Fine Spatio-Temporal Information Fusion (CF-STIF) network for enhancing the quality of compressed videos across multiple codecs, including HEVC, VVC, VP9, and AV1. The CF-STIF network leverages spatiotemporal fusion and deep learning techniques to reduce compression artifacts and improve video quality. The evaluation extends existing methods by employing multiple quality metrics such as PSNR, SSIM, LPIPS. The CF-STIF network has been integrated with the Spatio-Temporal Deformable Fusion (STDF) training scheme in order to execute the model. Results demonstrate that CF-STIF achieves the highest quality improvements for HEVC-encoded videos, with an average PSNR increase of 0.813 dB and superior visual quality as measured by SSIM. However, the performance significantly drops for other codecs, particularly AV1, highlighting the need for future adaptations to optimize CF-STIF for diverse compression standards.

关键词： Training Deep learning Adaptation models Visualization Codecs Computational modeling Computer architecture Quality assessment Standards videos

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GPU Acceleration of MIP Intra Prediction in VVC 31

GPU Acceleration of MIP Intra Prediction in VVC

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Storch, Iago Roma, Nuno Palomino, Daniel Bampi, Sergio Graduate Program in Computing Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil Lisboa Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Federal University of Pelotas Graduate Program in Computing Video Technology Research Group Pelotas Brazil

ISBN: (纸本)9789464593600

The Versatile video Coding (VVC) standard introduced several novel encoding tools for intra frame prediction, increasing the encoder complexity when compared to previous standards. Among these novelties is the MIP tool, whose acceleration has not been tackled in the literature. Therefore, an efficient parallelization of MIP prediction targeting GPU platforms is now proposed. The presented technique makes use of alternative reference samples and computes the distortion in an approximate manner to expose and potentiate massive parallelism. Moreover, the adopted prediction scheduling and memory communication were tailored by considering the GPUs' architecture and memory hierarchy. When compared with a CPU execution, this work is capable to accelerate the MIP prediction up to 105 times at the cost of a negligible coding efficiency loss of 0.284% BD-BR. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Graphics processing unit

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Fast VVC Test Zone Search and Affine Motion Estimation Using Machine Learning

Fast VVC Test Zone Search and Affine Motion Estimation Using...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Ramiro Viana Marta Loose Ruhan Conceição Marcelo Porto Guilherme Corrêa Luciano Agostini Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas/RS Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

As the demand for video transmission surges on remote work, education, and streaming services, the need for continuous advancements in video encoding technologies becomes increasingly evident. Adapting to the evolving requirements of efficient video delivery and consumption necessitates ongoing development and enhancement in video encoding standards, with Versatile video Coding (VVC) emerging as a notable example. This paper provides an overview of key algorithms within InterFrame prediction of VVC, mainly focusing on the Test Zone Search (TZS) and the Affine Motion Estimation (AME), two of the most computationally intensive tools inside the VVC. Furthermore, this paper introduces a fast TZS and AME approach using Machine Learning, specifically employing Decision Trees. The proposed approach achieved an average reduction of over $\mathbf{2 0 \%}$ in total VVC encoding time while maintaining less than a 1 % impact on BD-BR coding efficiency.

关键词： video coding Motion estimation Machine learning Streaming media Encoding Software Remote working Decision trees Surges Standards

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