PNeRV: A Polynomial Neural Representation for Videos
<|reference_start|>PNeRV: A Polynomial Neural Representation for Videos: Extracting Implicit Neural Representations (INRs) on video data poses unique challenges due to the additional temporal dimension. In the context of videos, INRs have predominantly relied on a frame-only parameterization, which sacrifices the spatiotemporal continuity observed in pixel-level (spatial) representations. To mitigate this, we introduce Polynomial Neural Representation for Videos (PNeRV), a parameter-wise efficient, patch-wise INR for videos that preserves spatiotemporal continuity. PNeRV leverages the modeling capabilities of Polynomial Neural Networks to perform the modulation of a continuous spatial (patch) signal with a continuous time (frame) signal. We further propose a custom Hierarchical Patch-wise Spatial Sampling Scheme that ensures spatial continuity while retaining parameter efficiency. We also employ a carefully designed Positional Embedding methodology to further enhance PNeRV's performance. Our extensive experimentation demonstrates that PNeRV outperforms the baselines in conventional Implicit Neural Representation tasks like compression along with downstream applications that require spatiotemporal continuity in the underlying representation. PNeRV not only addresses the challenges posed by video data in the realm of INRs but also opens new avenues for advanced video processing and analysis.<|reference_end|>
@article{gupta2024pnerv:,
    title={PNeRV: A Polynomial Neural Representation for Videos},
    author={Sonam Gupta, Snehal Singh Tomar, Grigorios G Chrysos, Sukhendu Das, A.
  N. Rajagopalan},
    journal={arXiv preprint arXiv:2406.19299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19299},
    primaryClass={cs.CV}
}