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.

Recently, implicit neural representation (INR)-based image compression has attracted growing attention due to its low decoding complexity. However, most existing methods require multiple encoding processes to generate multiple bitstreams and reconstructions at different quality levels, leading to high storage and computational overhead. A more practical solution is progressive compression, which supports multiple qualities within a single bitstream. In this paper, we propose PINIC (Progressive Implicit Neural Image Compression), a DWT-guided framework that integrates progressive functionality into INR-based compression for the first time. Specifically, DWT energy is applied to adaptively allocate the energy of latent channels and latent will be entropy coded in the order of channel energy. Moreover, we adopt reparameterization to decouple network structures between training and inference, retaining the expressive power of complex architectures while reducing parameter count at deployment. Extensive experiments demonstrate that PINIC achieves state-of-the-art rate-distortion performance on the Kodak and CLIC2020P datasets, while simultaneously supporting progressive compression.