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 maintaining its 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.

翻译：COMpression with Bayesian Implicit NEural Representations (COMBINER) 是一种最近提出的数据压缩方法，它解决了以往基于隐式神经表示（INR）方法的关键效率问题：该方法避免了量化过程，并能直接优化率失真性能。然而，COMBINER仍存在显著局限性：1）其使用的因式分解先验和后验近似缺乏灵活性；2）无法有效适应数据中全局模式的局部偏差；3）其性能易受模型选择及变分参数初始化的影响。我们提出的方法——鲁棒增强型COMBINER（RECOMBINER），通过以下方式解决这些问题：1）通过对INR权重进行线性重参数化，在保持计算成本的同时丰富变分近似；2）为INR增加可学习的位置编码，使其能够适应局部细节；3）将高分辨率数据分割为图像块以提升鲁棒性，并利用表达性层次先验捕捉跨图像块的依赖关系。我们在多种数据模态上进行了广泛实验，结果表明RECOMBINER不仅取得了与最优INR方法相匹敌的竞争性结果，在低比特率低分辨率图像上甚至超越了基于自编码器的编解码器。