From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression

In this paper, we propose a novel implicit neural representation (INR)-based image compression paradigm that generate image-specific latents from a multi-scale Gaussian noise tensor which is deterministically generated using a shared random seed. As shown in Fig. 1, inspired by the normalization strategy in E2E codecs where latents are transformed into Gaussian noise to demonstrate the removal of spatial redundancy, we explore the inverse direction: generating Gaussian latents directly from Gaussian noise. Specifically, given a noise input$z$, we introduce a Gaussian Parameter Prediction (GPP) module that estimates the Gaussian distribution parameters, enabling one-shot latent generation via the reparameterization trick. The predicted latent is then transformed into the reconstructed image through a synthesis network.