UI-LIC: A Unified Framework for Evaluating Learned Image Compression Models

2026-06-22

AI Summary

This work addresses the challenge of fair comparison in learned image compression (LIC), which has been hindered by inconsistent model implementations, training protocols, and evaluation metrics. To this end, we present UI-LIC, an open-source unified framework that integrates six state-of-the-art LIC models alongside traditional codecs within a consistent experimental setup, enabling end-to-end automated training, inference, and comparative evaluation. The framework features a graphical user interface supporting bitrate alignment, computation of multiple quality metrics—including PSNR, SSIM, VMAF, and LPIPS—and interactive visualization of quality heatmaps. Deployment and benchmarking require only a single command, substantially lowering the barrier to entry for researchers. The code is publicly released to foster reproducibility and further advancement in the field.

Abstract

The evaluation and comparison of Learned Image Compression (LIC) systems is complicated by heterogeneous software stacks, varying training conditions, and divergent evaluation methodologies. To address these challenges, we introduce UI-LIC, an open-source software framework for evaluating LIC models. We integrate six high-performance LIC models, and provide a centralized controller for performing training, inference, and analysis with shared configuration parameters. Our GUI program offers a streamlined interface to evaluate these models alongside traditional video intra-frame encoders, equalizing the compressed bitrates and calculating quality metrics such as PSNR, SSIM, VMAF, and LPIPS. Finally, we provide an interactive image analyzer with configurable quality heatmap overlays. Our framework lowers barriers to further LIC research, unlocking comparative metrics and subjective analysis with a single setup command. The open-source software is released under the MIT license and is available at github.com/BaylorMultimediaLab/UI-LIC.

Problem

• Learned Image Compression
• evaluation framework
• model comparison
• heterogeneous software stacks
• divergent evaluation methodologies

Innovation

• Learned Image Compression
• Unified Evaluation Framework
• Interactive Visualization
• Objective Quality Metrics
• Open-Source Software