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.

翻译：降低计算复杂度仍是基于学习的图像压缩技术广泛应用的关键挑战。本文提出TreeNet，一种新颖的低复杂度图像压缩模型，它利用二叉树结构的编码器-解码器架构来实现高效的表示与重建。我们采用注意力特征融合机制来有效整合来自多个分支的特征。我们在三个广泛使用的基准数据集上评估TreeNet，并将其性能与包括JPEG AI（一种近期基于学习的图像压缩标准）在内的竞争方法进行比较。在低比特率下，TreeNet相较于JPEG AI在BD-rate上平均提升了4.83%，同时模型复杂度降低了87.82%。此外，我们进行了广泛的消融研究，以探究TreeNet内不同潜在表示的影响，从而为影响重建效果的因素提供更深入的见解。