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| Title: | 4K UHD高解析度影像串流下的AI物件辨識加速器效能評測
Performance Evaluation on the AI Object Detection Accelerator for 4K UHD Video Streaming Application |
| Authors: | 謝瓊琪
Hsieh, Chiung-Chi |
| Contributors: | 張宏慶
Jang, Hung-Chin
謝瓊琪
Hsieh, Chiung-Chi |
| Keywords: | 高傳真4K影像串流
人工智慧推理
人工智慧推理運算加速器
效能評測
Ultra high-definition 4K video streaming
artificial intelligence inference
artificial intelligence inference accelerator
performance evaluation |
| Date: | 2023 |
| Issue Date: | 2023-09-01 15:38:57 (UTC+8) |
| Abstract: | 隨著影像編解碼技術的演進由H.264進入H.265、VP9，壓縮後的高傳真影像資料量可以降低近50%，加上有線/無線網路傳輸技術與設備規格大幅提升，網路傳輸成本降低，目前網路上傳輸佔比高達6.5成的流量是影像串流，影像解析度也由主流全高傳真1080 FHD (Full High Definition) 的圖像解析度提升至4K超高傳真UHD (Ultra High Definition)。目前現行人工智慧物件偵測與辨識擁有廣泛的應用，由特徵辨識到視頻分析等，物件檢測在安全、醫療、體育、交通、工廠等領域有關鍵應用。目前的視頻影像物件辨識與偵測研究大多聚焦於全高傳真FHD (1080p) 解析度，有鑒於視頻影像解析度日益提升，此份研究是以4K高傳真視頻影像串流 (4K Ultra High Definition Video Streaming) 應用爲出發，以NPU搭載不同的硬體架構 (CPU, GPGPU, SoC)及軟體流程，評測出較優的Video-Based的AI推理加速器架構。
With the evolution of video encoding and decoding technology from H.264 to H.265 and VP9, the compressed data size of high-resolution video can be reduced by nearly 50%. In addition, the significant improvements in wired/wireless network transmission technology and network device specifications on network bandwidth, the cost of network transmission is decreased gradually. At present, streaming video accounts for as much as 65% of the data traffic in the internet, and the video image resolution has been upgraded from the mainstream Full Hight Definition (FHD) 1080p to 4K Ultra High Definition (UHD). Currently, artificial intelligence object detection and recognition have a wide range of applications from character recognition to video analysis. Object detection plays a critical role in various industries such as security, healthcare, sports, transportation, and smart factory. Most of the current studies on artificial intelligence object recognition focus on FHD 1080p image resolution. Considering the increasing image resolution, this thesis focuses on 4K UHD video streaming applications for object detection. This study evaluates various hardware architectures (CPU, GPGPU, SoC) and correspondent software processes with NPU to determine the optimal Video-Based AI inference accelerator architecture. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
104971019 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0104971019 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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