Customizable ROI-Based Deep Image Compression
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Region of Interest (ROI)-based image compression optimizes bit allocation by
prioritizing ROI for higher-quality reconstruction. However, as the users
(including human clients and downstream machine tasks) become more diverse,
ROI-based image compression needs to be customizable to support various
preferences. For example, different users may define distinct ROI or require
different quality trade-offs between ROI and non-ROI. Existing ROI-based image
compression schemes predefine the ROI, making it unchangeable, and lack
effective mechanisms to balance reconstruction quality between ROI and non-ROI.
This work proposes a paradigm for customizable ROI-based deep image
compression. First, we develop a Text-controlled Mask Acquisition (TMA) module,
which allows users to easily customize their ROI for compression by just
inputting the corresponding semantic \emph{text}. It makes the encoder
controlled by text. Second, we design a Customizable Value Assign (CVA)
mechanism, which masks the non-ROI with a changeable extent decided by users
instead of a constant one to manage the reconstruction quality trade-off
between ROI and non-ROI. Finally, we present a Latent Mask Attention (LMA)
module, where the latent spatial prior of the mask and the latent
Rate-Distortion Optimization (RDO) prior of the image are extracted and fused
in the latent space, and further used to optimize the latent representation of
the source image. Experimental results demonstrate that our proposed
customizable ROI-based deep image compression paradigm effectively addresses
the needs of customization for ROI definition and mask acquisition as well as
the reconstruction quality trade-off management between the ROI and non-ROI.
