High-Performance Hardware/Software Co-Design for Text Extraction Using Optimized Gamma Correction in Video Decoding

IF 1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Ahmed Ben Atitallah, Meshari D. Alanazi, Turki M. Alanazi, Khaled Kaaniche, Mohammed Albekairi
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引用次数: 0

Abstract

This paper introduces an advanced Hardware/Software (HW/SW) co-design that makes it possible to extract text from decompressed video sequences in real-time, even when the videos are encoded using video coding. The design tackles the tricky computational challenges of video decoding and text extraction by embedding the Gamma Correction Method (GCM) directly into the video decoder. Furthermore, we develop an upgraded version of the GCM, known as the Optimized GCM, to enhance performance and reduce computational demands. Specialized hardware accelerators handle the computation-heavy parts of the optimized GCM. We then integrate these hardware modules as co-processors alongside the ARM Cortex-A53 processor via an AXI-stream interface. Finally, we tested the entire HW/SW system on the ZCU102 embedded kit. The proposed HW/SW co-design for the optimized GCM slashes processing time by a staggering 96% compared to a software-only implementation of the same approach. Even better, this time-saving boost doesn't sacrifice accuracy, as both methods achieve identical F-Measure values for text extraction. These findings showcase just how effective this HW/SW design is for handling the heavy computational workload of real-time text extraction from videos. They also highlight the potential of combining hardware accelerators with optimized algorithms to improve video processing systems in embedded environments. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

视频解码中使用优化伽马校正的文本提取的高性能硬件/软件协同设计
本文介绍了一种先进的硬件/软件(HW/SW)协同设计,即使使用视频编码对视频进行编码,也可以从解压的视频序列中实时提取文本。该设计通过将伽马校正方法(Gamma Correction Method, GCM)直接嵌入到视频解码器中,解决了视频解码和文本提取的棘手计算挑战。此外,我们还开发了GCM的升级版本,称为优化GCM,以提高性能并减少计算需求。专门的硬件加速器处理优化后的GCM中计算量大的部分。然后,我们通过轴流接口将这些硬件模块集成为ARM Cortex-A53处理器旁边的协处理器。最后,我们在ZCU102嵌入式套件上对整个软硬件系统进行了测试。与相同方法的纯软件实现相比,针对优化的GCM提出的硬件/软件协同设计将处理时间缩短了惊人的96%。更棒的是,这种节省时间的提升并不会牺牲准确性,因为两种方法在文本提取中获得相同的F-Measure值。这些发现表明,这种硬件/软件设计在处理从视频中实时提取文本的繁重计算工作量方面是多么有效。他们还强调了将硬件加速器与优化算法相结合的潜力,以改善嵌入式环境中的视频处理系统。©2025日本电气工程师协会和Wiley期刊有限责任公司。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEJ Transactions on Electrical and Electronic Engineering
IEEJ Transactions on Electrical and Electronic Engineering 工程技术-工程:电子与电气
CiteScore
2.70
自引率
10.00%
发文量
199
审稿时长
4.3 months
期刊介绍: IEEJ Transactions on Electrical and Electronic Engineering (hereinafter called TEEE ) publishes 6 times per year as an official journal of the Institute of Electrical Engineers of Japan (hereinafter "IEEJ"). This peer-reviewed journal contains original research papers and review articles on the most important and latest technological advances in core areas of Electrical and Electronic Engineering and in related disciplines. The journal also publishes short communications reporting on the results of the latest research activities TEEE ) aims to provide a new forum for IEEJ members in Japan as well as fellow researchers in Electrical and Electronic Engineering from around the world to exchange ideas and research findings.
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