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.
Abstract (translated)
降低计算复杂性仍然是基于学习的图像压缩技术广泛采用的关键挑战。在本文中，我们提出了TreeNet，这是一种新型低复杂度图像压缩模型，它利用二叉树结构的编解码器架构来实现高效的表示和重建。我们采用了注意特征融合机制，有效地整合了来自多个分支的特性。我们在三个常用的基准数据集上评估了TreeNet，并将其性能与包括JPEG AI在内的竞争方法进行了比较，JPEG AI是基于学习的图像压缩领域的最新标准之一。在低比特率下，TreeNet相较于JPEG AI实现了平均4.83%的BD-rate改进，同时将模型复杂度降低了87.82%。此外，我们还进行了广泛的消融研究，以探讨TreeNet中各种潜在表示的影响，提供了对重建因素更深入的理解。
URL
https://arxiv.org/abs/2512.16743
