PaaF: Raising the perceived quality of INR-Based Image Compression

• URL: http://arxiv.org/abs/2606.21655v1
• Date: Fri, 19 Jun 2026 18:00:37 GMT
• Title: PaaF: Raising the perceived quality of INR-Based Image Compression
• Abstract summary: Implicit Neural Representations (INRs) have emerged as a promising paradigm for image compression.<n>PaaF (Picture as a Function) is a novel INR-based image that introduces improved architectural design, adaptive quantization, and an efficient entropy coding scheme.<n> Experimental results demonstrate consistent improvements over existing INR-based methods in both quantitative metrics and perceptual quality.
• Score: 3.344552881770396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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