One is All: A Unified Rate-Distortion-Complexity Framework for Learned Image Compression Under Energy Concentration Criteria

Chao Li, Tianyi Li, Fanyang Meng, Qingyu Mao, Youneng Bao, Yonghong Tian, Yongsheng Liang

Abstract: The learned image compression (LIC) technique has surpassed the state-of-the-art traditional codecs (H.266/VVC) in case of rate-distortion (R-D) performance. Its real-time deployments are far advanced. In order to achieve more flexible deployments, an LIC technique should be flexible in adjusting its computational complexity and rate as demanded by a situation and its environment. In this paper, we propose a unified Rate-Distortion-Complexity (R-D-C) framework for LIC under channel energy concentration criteria. Specifically, we first introduce an Energy Asymptotic Nonlinear Transformation (EANT) designed to directly concentrate on the channel energy of latent representations, thus laying the groundwork for a scalable entropy coding. Next, leveraging this energy concentration characteristic, we propose a corresponding Heterogeneous Scalable Entropy Model (HSEM) for flexibly scaling bitstreams as needed. Finally, utilizing the proposed EANT, we construct a fine-grained scalable codec for formulating, in combination with HSEM, a comprehensive scalable R-D-C framework under the energy concentration criteria. The obtained experimental results demonstrate that the proposed method could enable seamless transitions between 13 different widths of sub-models within a single network, allowing for fine-grained control over the model bitrate, complexity, and hardware inference time. Additionally, the proposed method exhibits competitive R-D performance compared to many existing methods.

External IDs:doi:10.1109/tmm.2025.3535279