Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165689846
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