We propose an end-to-end learned image compression codec wherein the analysis transform is jointly trained with an object classification task. This study affirms that the compressed latent representation can predict human perceptual distance judgments with an accuracy comparable to a custom-tailored DNN-based quality metric. We further investigate various neural encoders and demonstrate the effectiveness of employing the analysis transform as a perceptual loss network for image tasks beyond quality judgments. Our experiments show that the off-the-shelf neural encoder proves proficient in perceptual modeling without needing an additional VGG network. We expect this research to serve as a valuable reference developing of a semantic-aware and coding-efficient neural encoder.
@article{arxiv.2401.07200,
title = {Exploring Compressed Image Representation as a Perceptual Proxy: A Study},
author = {Chen-Hsiu Huang and Ja-Ling Wu},
journal= {arXiv preprint arXiv:2401.07200},
year = {2024}
}