Title: PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Implicit Neural Representation, Image Compression

Abstract: Implicit neural representations (INRs) have recently demonstrated impressive performance in novel view synthesis and image/video compression, offering distinct advantages in terms of decoding complexity compared to conventional end-to-end image codecs. However, their broader adoption has been limited by slow encoding speeds and underutilization of their inherent potential for fast decoding --- often falling short of traditional codecs like JPEG. In this work, we present a fully end-to-end INR-based image compression framework, **P**ractical **I**NR Image **C**odec (PIC), that addresses both limitations. Our encoder computes all requisite parameters for the INR network in a single forward pass, achieving encoding speeds of over 20 FPS. To further unlock the decoding efficiency of INRs, we design an optimized decoder capable of real-time performance at up to 2000 FPS --- substantially exceeding JPEG decoding speed while delivering competitive or superior rate-distortion (RD) performance. To the best of our knowledge, this is the first learning-based image codec that simultaneously achieves practical encoding speed, outperforms JPEG in RD performancec, and surpasses it in decoding speed by a large margin. Code will be made publicly available.

TL;DR: An INR-based image codec capable of encoding at 20 FPS and decoding at 2000 FPS.