提出快速编码的隐式图像编码器，解码快过JPEG且质量不差。
PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding
- 单次前向传播完成编码所需全部计算，实现20帧每秒编码速度。
- 解码速度达2000帧每秒，显著超越JPEG在相同画质下的表现。
- 首个兼具高效编码与超快解码的端到端学习型图像压缩方案。
隐式神经表示（INR）近年来在新视角合成和图像/视频编码中取得显著进展。相比传统端到端图像编解码器，基于INR的压缩器在解码复杂度上具有明显优势。然而，其实际应用受限于编码速度慢和解码效率未被充分利用的问题。本文提出一种前馈式INR图像编码架构——实用隐式图像编解码器（PIC），可在一次前向传播中完成所有INR网络所需信息计算，实现20 FPS的编码速度。同时，我们设计了一个高度优化的解码器，达到2000 FPS的解码速度，在同等率失真（RD）性能下显著优于JPEG。据我们所知，这是首个在率失真性能和解码速度上均优于或相当媲美JPEG，且保持实用编码速度的学习型图像编解码器。代码已开源：https://github.com/actcwlf/PIC。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。