标题： 保持结构的补丁解码用于高效的神经视频表示 显示英文标题

标题： Structure-Preserving Patch Decoding for Efficient Neural Video Representation

摘要： 隐式神经表示（INRs）是广泛研究的主题，尤其是在通过将空间和时间坐标映射到相应值来建模复杂信号方面的应用。 在处理视频时，将紧凑输入映射到整个帧或空间划分的块图像是一种有效的方法。 与基于坐标的映射相比，这种策略更能保持空间关系，减少计算开销，并提高重建质量。 然而，预测整个帧通常会限制高频视觉细节的重建。 此外，基于均匀空间划分的传统块方法容易引入边界不连续性，从而降低空间一致性。 我们提出了一种基于结构保持块（SPPs）的神经视频表示方法，以解决这些限制。 我们的方法通过一种类似于PixelUnshuffle的确定性像素级分割，将每个视频帧分割为空间对齐的块图像。 此操作在保留全局空间结构的同时，允许块级解码。 我们训练解码器来重建这些结构化块，从而实现先捕捉全局布局再细化局部细节的全局到局部解码策略。 这有效地减少了边界伪影，并减轻了简单上采样的失真。 在标准视频数据集上的实验表明，我们的方法在重建质量和压缩性能方面优于现有的基于INR的基线方法。 显示英文摘要

摘要： Implicit neural representations (INRs) are the subject of extensive research, particularly in their application to modeling complex signals by mapping spatial and temporal coordinates to corresponding values. When handling videos, mapping compact inputs to entire frames or spatially partitioned patch images is an effective approach. This strategy better preserves spatial relationships, reduces computational overhead, and improves reconstruction quality compared to coordinate-based mapping. However, predicting entire frames often limits the reconstruction of high-frequency visual details. Additionally, conventional patch-based approaches based on uniform spatial partitioning tend to introduce boundary discontinuities that degrade spatial coherence. We propose a neural video representation method based on Structure-Preserving Patches (SPPs) to address such limitations. Our method separates each video frame into patch images of spatially aligned frames through a deterministic pixel-based splitting similar to PixelUnshuffle. This operation preserves the global spatial structure while allowing patch-level decoding. We train the decoder to reconstruct these structured patches, enabling a global-to-local decoding strategy that captures the global layout first and refines local details. This effectively reduces boundary artifacts and mitigates distortions from naive upsampling. Experiments on standard video datasets demonstrate that our method achieves higher reconstruction quality and better compression performance than existing INR-based baselines.