AI RESEARCH
Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss
arXiv CS.CV
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ArXi:2402.08267v3 Announce Type: replace Image coding for machines (ICM) aims to compress images for machine analysis using recognition models rather than human vision. Hence, in ICM, it is important for the encoder to recognize and compress the information necessary for the machine recognition task. There are two main approaches in learned ICM; optimization of the compression model based on task loss, and Region of Interest (ROI) based bit allocation. These approaches provide the encoder with the recognition capability.