Problem
Research questions and friction points this paper is trying to address.
Implicit Neural Representation
Image Coding
Encoding Speed
Decoding Efficiency
PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding
🤖 AI Summary
该研究提出了一种前馈INR图像编码架构PIC，解决了INR编码速度慢和解码效率低的问题，实现了快速编码与毫秒级解码。
📝 Abstract
Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years.Compared to conventional end-to-end image codecs, INR-based compressors demonstrate significant advantages in decoding complexity. However, their practical application has been hindered by the inferior encoding speed and underutilized decoding efficiency.In this work, we propose a feedforward INR image coding architecture, Practical INR Image Codec (PIC), that computes all the necessary information for INR network in a single forward pass, achieving an encoding speed of 20 FPS. Additionally, we implement a highly optimized decoder that reaches 2000 FPS decoding speed, significantly surpassing JPEG's performance at comparable rate-distortion (RD) performance. To the best of our knowledge, this work presents the first learning-based image codec that simultaneously outperforms or is comparable with JPEG in both RD performance and decoding speed while maintaining practical encoding speed. Code is available at https://github.com/actcwlf/PIC.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Implicit Neural Representation
Fast Encoding
Sub-Millisecond Decoding
Rate-Distortion Performance
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