ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression

TL;DR

ABC introduces a Bayesian network-based framework for adaptive, scalable neural image compression that dynamically manages computational complexity across all components, maintaining performance while accommodating diverse device and task requirements.

Contribution

The paper presents a novel Bayesian network structure learning approach for comprehensive computational scalability in neural image compression systems.

Findings

Achieves full computational scalability with better adaptivity.

Maintains competitive compression performance.

Enables integration with various NIC architectures.

Abstract

Neural Image Compression (NIC) has revolutionized image compression with its superior rate-distortion performance and multi-task capabilities, supporting both human visual perception and machine vision tasks. However, its widespread adoption is hindered by substantial computational demands. While existing approaches attempt to address this challenge through module-specific optimizations or pre-defined complexity levels, they lack comprehensive control over computational complexity. We present ABC (Adaptive BayesNet structure learning for computational scalable multi-task image Compression), a novel, comprehensive framework that achieves computational scalability across all NIC components through Bayesian network (BayesNet) structure learning. ABC introduces three key innovations: (i) a heterogeneous bipartite BayesNet (inter-node structure) for managing neural backbone computations; (ii) a homogeneous multipartite BayesNet (intra-node structure) for optimizing autoregressive unit processing; and (iii) an adaptive control module that dynamically adjusts the BayesNet structure based on device capabilities, input data complexity, and downstream task requirements. Experiments demonstrate that ABC enables full computational scalability with better complexity adaptivity and broader complexity control span, while maintaining competitive compression performance. Furthermore, the framework's versatility allows integration with various NIC architectures that employ BayesNet representations, making it a robust solution for ensuring computational scalability in NIC applications. Code is available in https://github.com/worldlife123/cbench_BaSIC.