ROI-based Deep Image Compression with Implicit Bit Allocation

The recent submission titled 'ROI-based Deep Image Compression with Implicit Bit Allocation' introduces a novel approach to image compression that prioritizes important regions while minimizing data redundancy. Traditional methods often apply masks to suppress background information before quantization, which can hinder coding performance due to their explicit bit allocation strategies. In contrast, this new model employs implicit bit allocation through a Mask-Guided Feature Enhancement module, which includes Region-Adaptive Attention and Frequency-Spatial Collaborative Attention blocks. This innovation allows for more flexible bit allocation and enhances both global and local features. The model also utilizes dual decoders to separately reconstruct foreground and background images, optimizing the coding network's performance. The research is based on the COCO2017 dataset and represents a significant advancement in the field of image compression, potentially improving applications in v…