Impacts of Image Compression on the Detection Quality of a Novel Real-Time Image Processing Platform

被引：0
|
作者：
Mehrke, Jannik [1 ]
Volk, Georg [2 ]
Stumpp, Yannik [1 ]
Bringmann, Oliver [2 ]
Terzis, Anestis [1 ]
机构：
[1] Ulm Univ Appl Sci, Inst Commun Technol, Ulm, Germany
[2] Univ Tubingen, Dept Comp Engn, Tubingen, Germany
来源：
2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC | 2023年
关键词：
D O I：
10.1109/ITSC57777.2023.10421873
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.
引用
收藏
页码：417 / 424
页数：8
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