COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representation (INR)-based approaches: it avoids quantization and enables direct optimization of the rate-distortion performance. However, COMBINER still has significant limitations: 1) it uses factorized priors and posterior approximations that lack flexibility; 2) it cannot effectively adapt to local deviations from global patterns in the data; and 3) its performance can be susceptible to modeling choices and the variational parameters' initializations. Our proposed method, Robust and Enhanced COMBINER (RECOMBINER), addresses these issues by 1) enriching the variational approximation while retaining a low computational cost via a linear reparameterization of the INR weights, 2) augmenting our INRs with learnable positional encodings that enable them to adapt to local details and 3) splitting high-resolution data into patches to increase robustness and utilizing expressive hierarchical priors to capture dependency across patches. We conduct extensive experiments across several data modalities, showcasing that RECOMBINER achieves competitive results with the best INR-based methods and even outperforms autoencoder-based codecs on low-resolution images at low bitrates. Our PyTorch implementation is available at https://github.com/cambridge-mlg/RECOMBINER/.

翻译：基于贝叶斯隐式神经表示的压缩方法（COMBINER）是近期提出的一种数据压缩技术，其解决了此前隐式神经表示（INR）方法的关键效率问题：避免了量化操作，并能够直接优化率失真性能。然而，COMBINER仍存在显著局限：(1) 其采用的分解式先验和后验近似缺乏灵活性；(2) 无法有效适应数据全局模式中的局部偏差；(3) 性能易受建模选择与变分参数初始化的影响。本文提出的鲁棒增强型COMBINER（RECOMBINER）通过以下策略解决上述问题：(1) 对INR权重采用线性重参数化，在保持低计算成本的同时丰富变分近似；(2) 为INR引入可学习位置编码，使其能够自适应局部细节；(3) 将高分辨率数据分割为图像块以提升鲁棒性，并利用层次先验表达块间依赖关系。我们在多种数据模态上进行了广泛实验，结果表明RECOMBINER在低比特率低分辨率图像任务中不仅取得了与最优INR方法相媲美的结果，甚至超越了基于自编码器的编解码方法。PyTorch实现代码已发布至 https://github.com/cambridge-mlg/RECOMBINER/。