SANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization

• URL: http://arxiv.org/abs/2510.15775v1
• Date: Fri, 17 Oct 2025 16:00:43 GMT
• Title: SANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization
• Authors: Gai Zhang, Xinfeng Zhang, Lv Tang, Hongyu An, Li Zhang, Qingming Huang,
• Abstract summary: We propose a Scene-Aware Neural Representation framework for light field image compression with end-to-end rate-distortion optimization.<n>For scene awareness, SANR introduces a hierarchical scene modeling block that leverages multi-scale latent codes to capture intrinsic scene structures.<n>Experiment results demonstrate that SANR significantly outperforms state-of-the-art techniques regarding rate-distortion performance with a 65.62% BD-rate saving against HEVC.
• Score: 54.184486302645716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Light field images capture multi-view scene information and play a crucial role in 3D scene reconstruction. However, their high-dimensional nature results in enormous data volumes, posing a significant challenge for efficient compression in practical storage and transmission scenarios. Although neural representation-based methods have shown promise in light field image compression, most approaches rely on direct coordinate-to-pixel mapping through implicit neural representation (INR), often neglecting the explicit modeling of scene structure. Moreover, they typically lack end-to-end rate-distortion optimization, limiting their compression efficiency. To address these limitations, we propose SANR, a Scene-Aware Neural Representation framework for light field image compression with end-to-end rate-distortion optimization. For scene awareness, SANR introduces a hierarchical scene modeling block that leverages multi-scale latent codes to capture intrinsic scene structures, thereby reducing the information gap between INR input coordinates and the target light field image. From a compression perspective, SANR is the first to incorporate entropy-constrained quantization-aware training (QAT) into neural representation-based light field image compression, enabling end-to-end rate-distortion optimization. Extensive experiment results demonstrate that SANR significantly outperforms state-of-the-art techniques regarding rate-distortion performance with a 65.62\% BD-rate saving against HEVC.

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