PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding
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 s
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “PIC: Revisiting INR for Image Coding with Fast Encoding and ” ≈ “Tongyi-MAI/Z-Image-Turbo””
- PossiblePossibly related (embedding) · 53%NeoMME: an efficient Multimodal-native and Multilingual Encoder →
- PossiblePossibly related (embedding) · 48%Introducing Gemma 4 12B: a unified, encoder-free multimodal model →
- LinkedLinked via arxiv author · 85%Yuxiang Liu →
“PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Jinxiang Wang →
“PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding”