PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding

Implicit neural representation (INR) has achieved remark-able progress in novel view synthesis and image/video coding in recentyears. Compared to conventional end-to-end image codecs, INR-basedcompressors demonstrate significant advantages in decoding complexity.However, their practical application has been hindered by the inferiorencoding speed and underutilized decoding efficiency. In this work, wepropose a feedforward INR image coding architecture, Practical INRImage Codec (PIC), that computes all the necessary information forINR network in a single forward pass, achieving an encoding speed of20 FPS. Additionally, we implement a highly optimized decoder thatreaches 2000 FPS decoding speed, significantly surpassing JPEG’s per-formance at comparable rate-distortion (RD) performance. To the best ofour knowledge, this work presents the first learning-based image codecthat simultaneously outperforms or is comparable with JPEG in bothRD performance and decoding speed while maintaining practical encod-ing speed. Code is available at https://github.com/actcwlf/PIC.