SEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression
This addresses the problem of inefficient compression for diverse image regions in computer vision applications, representing an incremental improvement over existing learned methods.
The paper tackles the limitation of single entropy models in learned lossless image compression by proposing SEEC, which uses semantic segmentation to guide multiple entropy models for different semantic regions, achieving state-of-the-art compression ratios with minimal latency.
Recently, learned image compression has attracted considerable attention due to its superior performance over traditional methods. However, most existing approaches employ a single entropy model to estimate the probability distribution of pixel values across the entire image, which limits their ability to capture the diverse statistical characteristics of different semantic regions. To overcome this limitation, we propose Segmentation-Assisted Multi-Entropy Models for Lossless Image Compression (SEEC). Our framework utilizes semantic segmentation to guide the selection and adaptation of multiple entropy models, enabling more accurate probability distribution estimation for distinct semantic regions. Specifically, SEEC first extracts image features and then applies semantic segmentation to identify different regions, each assigned a specialized entropy model to better capture its unique statistical properties. Finally, a multi-channel discrete logistic mixture likelihood is employed to model the pixel value distributions effectively. Experimental results on benchmark datasets demonstrate that SEEC achieves state-of-the-art compression ratios while introducing only minimal encoding and decoding latency. With superior performance, the proposed model also supports Regions of Interest (ROIs) coding condition on the provided segmentation mask. Our code is available at https://github.com/chunbaobao/SEEC.