Luc Trudeau

Nicholas J. Nolen, Luc Trudeau, Andrew C. Freeman

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.

Luc N. Trudeau, Nathan E. Egge, David Barr

Chroma from luma (CfL) prediction is a new and promising chroma-only intra predictor that models chroma pixels as a linear function of the coincident reconstructed luma pixels. In this paper, we present the CfL predictor adopted in Alliance Video 1 (AV1), a royalty-free video codec developed by the Alliance for Open Media (AOM). The proposed CfL distinguishes itself from prior art not only by reducing decoder complexity, but also by producing more accurate predictions. On average, CfL reduces the BD-rate, when measured with CIEDE2000, by 5% for still images and 2% for video sequences.