Preprocessing Enhanced Image Compression for Machine Vision

• URL: http://arxiv.org/abs/2206.05650v1
• Date: Sun, 12 Jun 2022 03:36:38 GMT
• Title: Preprocessing Enhanced Image Compression for Machine Vision
• Authors: Guo Lu, Xingtong Ge, Tianxiong Zhong, Jing Geng, Qiang Hu
• Abstract summary: We propose a preprocessing enhanced image compression method for machine vision tasks. Our framework is built upon the traditional non-differential codecs. Experimental results show our method achieves a better tradeoff between the coding and the performance of the downstream machine vision tasks by saving about 20%.
• Score: 14.895698385236937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, more and more images are compressed and sent to the back-end devices for the machine analysis tasks~(\textit{e.g.,} object detection) instead of being purely watched by humans. However, most traditional or learned image codecs are designed to minimize the distortion of the human visual system without considering the increased demand from machine vision systems. In this work, we propose a preprocessing enhanced image compression method for machine vision tasks to address this challenge. Instead of relying on the learned image codecs for end-to-end optimization, our framework is built upon the traditional non-differential codecs, which means it is standard compatible and can be easily deployed in practical applications. Specifically, we propose a neural preprocessing module before the encoder to maintain the useful semantic information for the downstream tasks and suppress the irrelevant information for bitrate saving. Furthermore, our neural preprocessing module is quantization adaptive and can be used in different compression ratios. More importantly, to jointly optimize the preprocessing module with the downstream machine vision tasks, we introduce the proxy network for the traditional non-differential codecs in the back-propagation stage. We provide extensive experiments by evaluating our compression method for two representative downstream tasks with different backbone networks. Experimental results show our method achieves a better trade-off between the coding bitrate and the performance of the downstream machine vision tasks by saving about 20% bitrate.

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