Category-Dependent Learned Image Compression for Smartphone Photography with Standard-Compliant Decoders

In this paper, we address the need for tailored image compression by focusing on the specific use case of smartphone photography, particularly selfie, food and landscape images, which dominate user-captured photos. We adapt SegPIC (Segmentation Prior Image Compression) by fine-tuning it individually on a dedicated selfie, food and landscape dataset while keeping the decoder unchanged to maintain compatibility with JPEG AI standard decoding requirements. The model’s performance was evaluated using Kodak dataset and the JPEG AI test set, with comparisons based on PSNR and MS-SSIM metrics. To assess the effectiveness of categoryspecific fine-tuning method, we evaluated the MBT model (Minnen’s et al.’s) across the same categories. Our results demonstrate that this fine-tuning improves compression efficiency and image quality compared to training on general datasets (achieving up to 8.2 % BD-rate reduction compared to training on general datasets), highlighting the benefits of category-specific training within standardized frameworks.