PNeRV: Enhancing Spatial Consistency via Pyramidal Neural Representation for Videos

Qi Zhao, M. Salman Asif, Zhan Ma; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19103-19112

The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However current NeRV systems often face a significant issue of spatial inconsistency leading to decreased perceptual quality. To address this issue we introduce the Pyramidal Neural Representation for Videos (PNeRV) which is built on a multi-scale information connection and comprises a lightweight rescaling operator Kronecker Fully-connected layer (KFc) and a Benign Selective Memory (BSM) mechanism. The KFc inspired by the tensor decomposition of the vanilla Fully-connected layer facilitates low-cost rescaling and global correlation modeling. BSM merges high-level features with granular ones adaptively. Furthermore we provide an analysis based on the Universal Approximation Theory of the NeRV system and validate the effectiveness of the proposed PNeRV. We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD). Compared to vanilla NeRV PNeRV achieves a +4.49 dB gain in PSNR and a 231% increase in FVD on UVG along with a +3.28 dB PSNR and 634% FVD increase on DAVIS.