Multiresolution Contexts for Implicit Neural Codecs

Autoencoder-based Learned Image Codecs (LICs) typically exhibit similar computational complexities for both encoding and decoding processes. In contrast, the emerging paradigm of Implicit Neural Codecs (INCs) focuses on overfitting compact neural networks to source images, resulting in highly efficient decoders at the expense of prolonged encoding times. These approaches often leverage multiresolution latent representations and employ entropy models that utilize neighborhood context. In this work, we propose a method that harnesses multiresolution neighborhoods to reduce redundancy and improve coding efficiency. Importantly, our approach maintains the computational complexity of both encoding and decoding while achieving up to a 2.7% saving in BD-Rate on the KODAK dataset. Code is available at github.com/caribankai/MRC-Cool-Chic.