发表机构
Tsinghua University; Peng Cheng Laboratory; Harbin Institute of Technology, Shenzhen; Joy Future Academy, JD Group（清华大学; 鹏城实验室; 哈尔滨工业大学（深圳）; 京东探索研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PIC，一种前馈式隐式神经表示图像编解码器，实现20 FPS编码和2000 FPS解码，在率失真性能和解码速度上均优于或媲美JPEG。
AI 中文摘要
隐式神经表示（INR）近年来在新视图合成和图像/视频编码领域取得了显著进展。与传统的端到端图像编解码器相比，基于INR的压缩器在解码复杂度方面展现出明显优势。然而，其实际应用一直受到编码速度较慢和解码能力未充分利用的阻碍。在这项工作中，我们提出了一种前馈式INR图像编码架构——实用INR图像编解码器（PIC），该架构通过单次前向传播计算INR网络所需的全部信息，实现了20 FPS的编码速度。此外，我们实现了一个高度优化的解码器，其解码速度达到2000 FPS，在相近的率失真（RD）性能下显著超越了JPEG。据我们所知，这项工作首次提出了一个学习型图像编解码器，其在RD性能和解码速度上同时优于或可与JPEG相媲美，同时保持了实用的编码速度。代码可在提供的网址获取。
英文摘要
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.
CommentsAccepted at ECCV 2026. Code is available at https://github.com/actcwlf/PIC
