ROI-based Deep Image Compression with Implicit Bit Allocation

TL;DR

This paper introduces a novel deep image compression method that uses implicit bit allocation guided by ROI masks, enhancing coding efficiency and quality in important regions without explicit masking.

Contribution

It proposes a new Mask-Guided Feature Enhancement module and dual decoders for implicit bit allocation, improving rate-distortion performance over explicit methods.

Findings

Outperforms explicit bit allocation methods on COCO2017 dataset

Achieves higher rate-distortion efficiency with better visual quality

First to utilize implicit bit allocation for region-adaptive coding

Abstract

Region of Interest (ROI)-based image compression has rapidly developed due to its ability to maintain high fidelity in important regions while reducing data redundancy. However, existing compression methods primarily apply masks to suppress background information before quantization. This explicit bit allocation strategy, which uses hard gating, significantly impacts the statistical distribution of the entropy model, thereby limiting the coding performance of the compression model. In response, this work proposes an efficient ROI-based deep image compression model with implicit bit allocation. To better utilize ROI masks for implicit bit allocation, this paper proposes a novel Mask-Guided Feature Enhancement (MGFE) module, comprising a Region-Adaptive Attention (RAA) block and a Frequency-Spatial Collaborative Attention (FSCA) block. This module allows for flexible bit allocation across different regions while enhancing global and local features through frequencyspatial domain collaboration. Additionally, we use dual decoders to separately reconstruct foreground and background images, enabling the coding network to optimally balance foreground enhancement and background quality preservation in a datadriven manner. To the best of our knowledge, this is the first work to utilize implicit bit allocation for high-quality regionadaptive coding. Experiments on the COCO2017 dataset show that our implicit-based image compression method significantly outperforms explicit bit allocation approaches in rate-distortion performance, achieving optimal results while maintaining satisfactory visual quality in the reconstructed background regions.