Exploring Compressed Image Representation as a Perceptual Proxy: A Study
Chen-Hsiu Huang Ja-Ling Wu
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.