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复杂视频编码解码器在嵌入式系统和通用处理器上的分析。

Complexity Analysis of a Versatile Video Coding Decoder over Embedded Systems and General Purpose Processors.

机构信息

Software Technologies and Multimedia Systems for Sustainability (CITSEM) Research Center, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain.

出版信息

Sensors (Basel). 2021 May 11;21(10):3320. doi: 10.3390/s21103320.

DOI:10.3390/s21103320
PMID:34064767
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8151641/
Abstract

The increase in high-quality video consumption requires increasingly efficient video coding algorithms. Versatile video coding (VVC) is the current state-of-the-art video coding standard. Compared to the previous video standard, high efficiency video coding (HEVC), VVC demands approximately 50% higher video compression while maintaining the same quality and significantly increasing the computational complexity. In this study, coarse-grain profiling of a VVC decoder over two different platforms was performed: One platform was based on a high-performance general purpose processor (HGPP), and the other platform was based on an embedded general purpose processor (EGPP). For the most intensive computational modules, fine-grain profiling was also performed. The results allowed the identification of the most intensive computational modules necessary to carry out subsequent acceleration processes. Additionally, the correlation between the performance of each module on both platforms was determined to identify the influence of the hardware architecture.

摘要

高质量视频消费的增加需要越来越高效的视频编码算法。多功能视频编码 (VVC) 是当前最先进的视频编码标准。与之前的视频标准高效视频编码 (HEVC) 相比，VVC 在保持相同质量的同时，要求视频压缩率提高约 50%，同时显著增加了计算复杂度。在这项研究中，对两个不同平台上的 VVC 解码器进行了粗粒度分析：一个平台基于高性能通用处理器 (HGPP)，另一个平台基于嵌入式通用处理器 (EGPP)。对于最密集的计算模块，还进行了细粒度分析。结果允许确定进行后续加速过程所需的最密集的计算模块。此外，还确定了两个平台上每个模块性能之间的相关性，以确定硬件架构的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/a001c404d77a/sensors-21-03320-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/8a3e03c1e487/sensors-21-03320-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/3d0d6da81d21/sensors-21-03320-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/704fa842473c/sensors-21-03320-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/2177f8b05807/sensors-21-03320-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/fe12be17dd8d/sensors-21-03320-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/d0c9191c9b4a/sensors-21-03320-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/a001c404d77a/sensors-21-03320-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/8a3e03c1e487/sensors-21-03320-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/3d0d6da81d21/sensors-21-03320-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/704fa842473c/sensors-21-03320-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/2177f8b05807/sensors-21-03320-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/fe12be17dd8d/sensors-21-03320-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/d0c9191c9b4a/sensors-21-03320-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58ac/8151641/a001c404d77a/sensors-21-03320-g008.jpg

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本文引用的文献

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Tunable VVC Frame Partitioning based on Lightweight Machine Learning.基于轻量级机器学习的可调式可变视频编码（VVC）帧分区
IEEE Trans Image Process. 2019 Sep 6. doi: 10.1109/TIP.2019.2938670.