PaaF提升INR图像压缩质量，兼顾速度与视觉效果。
PaaF: Raising the perceived quality of INR-Based Image Compression
- 设计新架构+自适应量化+高效熵编码，优化INR压缩流程。
- 在PSNR和感知质量上均超越现有INR方法，提升显著。
- 适合关注神经表示压缩、追求高效解码的开发者参考。
隐式神经表示（INRs）为图像压缩提供了新颖范式，但现有方法存在编码时间长、传统质量指标（如PSNR）表现不佳的问题。本文提出PaaF（Picture as a Function），一种纯INR基础的图像编解码器，引入改进的网络结构、自适应量化和高效的熵编码方案。这些设计在保持INR解码简单性和并行性的同时，显著提升了率失真性能。实验表明，PaaF在定量指标和感知质量上均持续优于现有INR方法，凸显了其在功能表示与成熟压缩范式之间缩小差距的潜力。
原文摘要 · Abstract (English)
Implicit Neural Representations (INRs) have recently emerged as a promising paradigm for image compression, offering a fundamentally different approach from traditional and learned codecs. Nevertheless, INR-based methods for image compression suffer from long encoding times and a consistent performance gap in classic quality metrics such as PSNR. In this work, we explore the potential of purely INR-based compression methods and we propose PaaF (Picture as a Function), a novel INR-based image codec that introduces improved architectural design, adaptive quantization, and an efficient entropy coding scheme. These components are designed to enhance rate-distortion performance while preserving the simplicity and parallelizability of INR-based decoding. Experimental results demonstrate consistent improvements over existing INR-based methods in both quantitative metrics and perceptual quality. These findings highlight the potential of INR-based approaches and contribute to narrowing the gap between functional representations and more established compression paradigms.
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