{"ID":2825326,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20871","arxiv_id":"2512.20871","title":"NeRV360: Neural Representation for 360-Degree Videos with a Viewport Decoder","abstract":"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.","short_abstract":"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-s...","url_abs":"https://arxiv.org/abs/2512.20871","url_pdf":"https://arxiv.org/pdf/2512.20871v2","authors":"[\"Daichi Arai\",\"Kyohei Unno\",\"Yasuko Sugito\",\"Yuichi Kusakabe\"]","published":"2025-12-24T01:21:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.MM\",\"eess.IV\"]","methods":"[]","has_code":false}