ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation

TL;DR

ARCHE is a novel image compression framework that combines hierarchical, spatial, and channel priors with adaptive feature recalibration, achieving state-of-the-art rate-distortion performance efficiently without recurrent or transformer modules.

Contribution

It introduces a unified probabilistic framework for image compression that balances modeling accuracy and computational efficiency, surpassing existing methods in rate-distortion metrics.

Findings

Reduces BD-Rate by ~48% compared to Balle et al.

Achieves 30% improvement over Minnen & Singh's autoregressive model.

Maintains computational efficiency with 95M parameters and 222ms per image.

Abstract

Recent progress in learning-based image compression has demonstrated that end-to-end optimization can substantially outperform traditional codecs by jointly learning compact latent representations and probabilistic entropy models. However, many existing approaches achieve high rate-distortion efficiency at the expense of increased computational cost and limited parallelism. This paper presents ARCHE - Autoregressive Residual Compression with Hyperprior and Excitation, an end-to-end learned image compression framework that balances modeling accuracy and computational efficiency. The proposed architecture unifies hierarchical, spatial, and channel-based priors within a single probabilistic framework, capturing both global and local dependencies in the latent representation of the image, while employing adaptive feature recalibration and residual refinement to enhance latent representation quality. Without relying on recurrent or transformer-based components, ARCHE attains state-of-the-art rate-distortion efficiency: it reduces the BD-Rate by approximately 48% relative to the commonly used benchmark model of Balle et al., 30% relative to the channel-wise autoregressive model of Minnen & Singh and 5% against the VVC Intra codec on the Kodak benchmark dataset. The framework maintains computational efficiency with 95M parameters and 222ms running time per image. Visual comparisons confirm sharper textures and improved color fidelity, particularly at lower bit rates, demonstrating that accurate entropy modeling can be achieved through efficient convolutional designs suitable for practical deployment.