Implicit neural representations for videos (NeRV) have shown strong potential for video compression. However, applying NeRV to high-resolution 360-degree videos causes high memory usage and slow decoding, making real-time applications impractical. We propose NeRV360, an end-to-end framework that decodes only the user-selected viewport instead of reconstructing the entire panoramic frame. Unlike conventional pipelines, NeRV360 integrates viewport extraction into decoding and introduces a spatial-temporal affine transform module for conditional decoding based on viewpoint and time. Experiments on 6K-resolution videos show that NeRV360 achieves a 7-fold reduction in memory consumption and a 2.5-fold increase in decoding speed compared to HNeRV, a representative prior work, while delivering better image quality in terms of objective metrics.
@article{arxiv.2512.20871,
title = {NeRV360: Neural Representation for 360-Degree Videos with a Viewport Decoder},
author = {Daichi Arai and Kyohei Unno and Yasuko Sugito and Yuichi Kusakabe},
journal= {arXiv preprint arXiv:2512.20871},
year = {2026}
}
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2026 IIEEJ International Conference on Image Electronics and Visual Computing (IEVC)