PT Journal
AU Mingyi Yang
   Fei Yang
   Luka Murn
   Marc Gorriz Blanch
   Juil Sock
   Shuai Wan
   Fuzheng Yang
   Luis Herranz
TI Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks
SO IEEE Transactions on Circuits and Systems for Video Technology
PY 2024
DI 10.1109/TCSVT.2023.3348995
DE M Yang; F Yang; L Murn; MG Blanch; J Sock; S Wan; L Herranz
AB Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications.
ER