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

AI summary

Overview

Research Area: Computer Vision Technical Level: Beginner-Friendly Reading Time: ~3 minutes Published: September 8, 2026

What This Paper Is About

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.

Key Contributions

1. Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years
2. 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
3. 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

Main Findings

• Finding 1: Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years
• Finding 2: Compared to conventional end-to-end image codecs, INR-based compressors demonstrate significant advantages in decoding complexity
• Finding 3: 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

Methodology in Plain English

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

Why This Matters

Impact on Research: This work opens new directions for researchers in Computer Vision.

Real-World Applications:

• Improving AI system performance and reliability
• Developing more efficient and scalable solutions
• Addressing practical challenges in Computer Vision
• Enabling new capabilities in AI-powered applications

Industry Relevance: Companies working with AI can leverage these findings to enhance their products and reduce development friction.

Future Directions

• Extending the approach to related domains and use cases
• Improving scalability and efficiency of the proposed methods
• Investigating practical deployment considerations
• Exploring combinations with complementary techniques

Target Audience

• Researchers in Computer Vision and related fields
• Engineers building AI systems and applications
• Students studying advanced topics in artificial intelligence
• Industry practitioners seeking to apply cutting-edge research

This summary was generated from the paper abstract. For full technical details, refer to the original paper.

Authors’ 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.