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Binary-NeRV: Hybrid-Precision Weights Binarization for Efficient Neural Video Representation
Shanableh, Tamer
Shanableh, Tamer
Date
2026
Author
Advisor
Type
Article
Peer-Reviewed
Published version
Peer-Reviewed
Published version
Degree
Description
Abstract
Neural implicit video representations such as NeRV have emerged as a powerful alternative to traditional video codecs. However, the high computational cost and full-precision storage of NeRV limit its practicality for resource-constrained and embedded platforms. In this work, we propose Binary-NeRV, a hybrid-precision extension of NeRV that integrates XNOR-based binary convolutions into the decoding pipeline to significantly reduce model size, bitrate, and computational complexity while reasonably preserving reconstruction fidelity. Inspired by XNOR-Net, convolutional weights are binarized to ±1 with learned scaling factors, while critical components such as the stem MLP, normalization layers, activations, skip connections, and upsampling operators are selectively retained in FP32 to ensure stable training and high visual quality. We introduce two directional progressive binarization strategies, Left-to-Right (L2R) and Right-to-Left (R2L), to analyze the impact of binarizing layers at different spatial resolutions. A detailed complexity analysis shows that over 75% of NeRV’s computational cost is dominated by the final high-resolution convolutional layer, enabling highly effective targeted binarization. Extensive experiments on standard video sequences demonstrate that selectively binarizing only the deepest layer achieves up to 68–89% reduction in equivalent GFLOPs with reasonable degradation in PSNR and MS-SSIM. Temporal consistency analysis shows that selective binarization preserves temporal stability, while aggressive full-component binarization introduces noticeable flickering artifacts. We additionally present an ablation study where all convolutional layers are binarized, both before and after pruning and quantization. These results systematically validate the hybrid-precision design, showing that full binarization yields substantial bitrate reductions but at a notable quality cost, thereby justifying the selective binarization strategy adopted in Binary-NeRV. Compared with state-of-the-art NeRV variants, Binary-NeRV delivers substantial efficiency gains, establishing hybrid binarization as a practical and scalable approach for efficient neural video representation.