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

Jannik Mehrke,Georg Volk, Yannik Stumpp,Oliver Bringmann, Anestis Terzis

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(2023)

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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.
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关键词
Image Processing,Real-time Performance,Image Compression,Processing Platform,Real-time Image Processing,Image Processing Platform,Data Processing,Neural Network,Data Transmission,Modern Imaging,Compression Algorithm,Image Encoder,Cloud-based Data,Local System,Object Detection,Bounding Box,System Quality,Time Requirements,Fast Imaging,Bit Error,Packet Loss,Cloud System,Single Shot Multibox Detector,5G Networks,High Compression,Compression Ratio,KITTI Dataset,4G Networks,Continuous Video,Connectivity Parameters
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