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OpenFastVC: An Open Source Library for Video Coding Fast Algorithm Implementation

Published: 27 October 2023 Publication History

Abstract

Despite the remarkable coding gains exhibited by the recently released new-generation video coding standards, their serious computational complexity will pose a significant challenge in coding latency to practical applications. Therefore, the corresponding low-complexity optimizations assume paramount importance. To facilitate the research in this field, the first open source software library for video coding fast algorithm implementation, namely OpenFastVC, is proposed in this paper. Specifically, OpenFastVC offers the outputting and processing of the intermediate coding information, e.g., the CU partitioning results, which is indispensable to fast algorithm design. To facilitate the integration of the designed algorithms, OpenFastVC also provides application programming interfaces (APIs) for direct control over the encoding process. Moreover, the existing typical fast algorithms are further implemented in OpenFastVC, enabling researchers to evaluate the performance of their algorithm effortlessly. The release of this library is highly favorable for the design, implementation, and evaluation of video coding fast algorithms, thereby fostering the widespread adoption of the new coding standards. The open source library for OpenFastVC is available at https://openi.pcl.ac.cn/OpenCompression/OpenFastVC.

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  • (2024)Divide-and-conquer-based RDO-free CU Partitioning for 8K Video CompressionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363470520:4(1-20)Online publication date: 11-Jan-2024
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cover image ACM Conferences
MM '23: Proceedings of the 31st ACM International Conference on Multimedia
October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 27 October 2023

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Author Tags

  1. fast algorithm
  2. low-complexity optimization
  3. open source software
  4. video coding

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MM '23
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MM '23: The 31st ACM International Conference on Multimedia
October 29 - November 3, 2023
Ottawa ON, Canada

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2024)Divide-and-conquer-based RDO-free CU Partitioning for 8K Video CompressionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363470520:4(1-20)Online publication date: 11-Jan-2024
  • (2024)Adaptive Intra Period Size for Deep Learning-Based Screen Content Video Coding2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW63481.2024.10645479(1-6)Online publication date: 15-Jul-2024
  • (2024)Open-Source Projects for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_9(255-272)Online publication date: 10-Oct-2024
  • (2024)Point Cloud-Language Multi-modal LearningDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_8(227-254)Online publication date: 10-Oct-2024
  • (2024)Point Cloud Pre-trained Models and Large ModelsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_7(195-225)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-Based Point Cloud Analysis IIDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_6(163-193)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-Based Point Cloud Analysis IDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_5(131-162)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-Based Point Cloud Enhancement IIDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_4(99-130)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-based Point Cloud Enhancement IDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_3(71-97)Online publication date: 10-Oct-2024
  • (2024)Learning Basics for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_2(29-70)Online publication date: 10-Oct-2024
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