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 tailored to its local distribution. This region-wise design enhances adaptation to local statistics and enables flexible, single-object compression with fine-grained ratedistortion (RD) control. MoRIC allows regions of arbitrary shapes, and provides the contour information for each region as separate information. In particular, it incorporates 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 compression through selective region refinement, paving the way for scalable and flexible INR-based codecs.

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 tailored 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 incorporates 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 compression through selective region refinement, paving the way for scalable and flexible INR-based codecs.

Recent advances in implicit neural representation (INR)-based video coding havedemonstrated its potential to compete with both conventional and other learning-based approaches. With INR methods, a neural network is trained to overfit avideo sequence, with its parameters compressed to obtain a compact representationof the video content. However, although promising results have been achieved,the best INR-based methods are still out-performed by the latest standard codecs,such as VVC VTM, partially due to the simple model compression techniquesemployed. In this paper, rather than focusing on representation architectures, whichis a common focus in many existing works, we propose a novel INR-based videocompression framework, Neural Video Representation Compression (NVRC),targeting compression of the representation. Based on its novel quantization andentropy coding approaches, NVRC is the first framework capable of optimizing anINR-based video representation in a fully end-to-end manner for the rate-distortiontrade-off. To further minimize the additional bitrate overhead introduced by theentropy models, NVRC also compresses all the network, quantization and entropymodel parameters hierarchically.

Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods.

It is a variant of shuffle coding that is many orders of magnitude faster than the original and enables'one-shot' compression of single

Our approach builds upon variational autoencoder (V AE) models for sequential data and combines them with recent work on neural image compression.

Dear Reviewers, we thank you for taking the time to provide valuable feedback. Below we address the main issues raised. Its performance depends on our ability to predict the distribution over future frames with low entropy. We will emphasize these aspects more in a revised version. RNNs to model dynamics in the latent space.

Language models have been successfully used to model natural signals, such as images, speech, and music.