Complexity analysis of the MoRIC
Fig. 2: RD performance for standard image compression. BD-rate vs. decoding complexity on (a) Davis. (b) Kodak, and (c) CLIC2020 datasets.
We introduce Modular Region-Based Implicit Codec (MoRIC), a novel image compression algorithm that relies on implicit neural representations (INRs). Unlike previous INR-based codecs that model the entire image with a single neural network, MoRIC assigns dedicated models to distinct regions in the image, each tai- lored to its local distribution. This region-wise design enhances adaptation to local statistics and enables flexible, single-object compression with fine-grained rate- distortion (RD) control. MoRIC allows regions of arbitrary shapes, and provides the contour information for each region as separate information. In particular, it in- corporates adaptive chain coding for lossy and lossless contour compression, and a shared global modulator that injects multi-scale global context into local overfitting processes in a coarse-to-fine manner. MoRIC achieves state-of-the-art performance in single-object compression with significantly lower decoding complexity than existing learned neural codecs, which results in a highly efficient compression approach for fixed-background scenarios, e.g., for surveillance cameras. It also sets a new benchmark among overfitted codecs for standard image compression. Additionally, MoRIC naturally supports semantically meaningful layered compres- sion through selective region refinement, paving the way for scalable and flexible INR-based codecs.
Fig. 1: llustration of the MoRIC framework.
Fig. 2: RD performance for standard image compression. BD-rate vs. decoding complexity on (a) Davis. (b) Kodak, and (c) CLIC2020 datasets.
Fig. 3: The image is partitioned into \( N \) regions, each compressed using a distinct LSN \( g_{\hat{\boldsymbol{W}}^i} \) modulated with a shared GMN \( f_{\hat{\boldsymbol{\theta}}} \).
Fig. 4: Red blocks with shared icons indicate parameter-shared components across regions. Each LSN is modulated using multi-scale features generated by \( f_{\hat{\boldsymbol{\theta}}} \) from the corresponding latent vector \( \hat{\boldsymbol{z}}^i \).
Fig. 5: RD performance for standard image compression. (a) RD curve and BD rate on Davis. (b) RD curve and BD rate on Kodak. (c) RD curve and BD rate on CLIC2020.
@article{MoRIC,
title={MoRIC: A Modular Region-based Implicit Codec for Image Compression},
author={Gen Li, Haotian Wu, and Deniz Gündüz},
journal={Conference on Neural Information Processing Systems},
year={2025}
}