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arXiv · 2512.16743

TreeNet: A Light Weight Model for Low Bitrate Image Compression

Mahadev Prasad Panda·Purnachandra Rao Makkena·Srivatsa Prativadibhayankaram·Siegfried Fößel·André Kaup
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Abstract

Reducing computational complexity remains a critical challenge for the widespread adoption of learning-based image compression techniques. In this work, we propose TreeNet, a novel low-complexity image compression model that leverages a binary tree-structured encoder-decoder architecture to achieve efficient representation and reconstruction. We employ attentional feature fusion mechanism to effectively integrate features from multiple branches. We evaluate TreeNet on three widely used benchmark datasets and compare its performance against competing methods including JPEG AI, a recent standard in learning-based image compression. At low bitrates, TreeNet achieves an average improvement of 4.83% in BD-rate over JPEG AI, while reducing model complexity by 87.82%. Furthermore, we conduct extensive ablation studies to investigate the influence of various latent representations within TreeNet, offering deeper insights into the factors contributing to reconstruction.

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Mahadev Prasad Panda, Purnachandra Rao Makkena, Srivatsa Prativadibhayankaram, Siegfried Fößel, André Kaup. 2025-12-18. TreeNet: A Light Weight Model for Low Bitrate Image Compression. https://arxiv.org/abs/2512.16743

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