Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions

Neural video representation (NeRV) has emerged as an efficient paradigm for video compression by encoding entire sequences into neural network parameters. Despite its strong reconstruction capability, NeRV suffers from high computational cost due to expensive convolutional operations in the decoder, limiting its applicability in resource-constrained environments. In this work, we propose Ghost-NeRV, an efficient extension of NeRV that reduces computational complexity through architectural optimization rather than numerical approximation. Ghost-NeRV integrates Ghost convolutions into the NeRV decoder to exploit feature redundancy and generate expressive representations using low-cost operations. Unlike binarization-based approaches, Ghost-NeRV preserves full-precision computation while significantly reducing the number of floating-point operations and model parameters. Extensive experiments on multiple video sequences demonstrate that Ghost-NeRV achieves up to 50% reduction in GFLOPs and approximately 19% reduction in model size, while maintaining stable reconstruction quality. Compared to the NeRV baseline, Ghost-NeRV incurs only a modest degradation in PSNR (typically within 1 dB) and preserves temporal consistency, significantly outperforming Binary-NeRV in perceptual stability. An ablation study further evaluates a GhostConv-V2 variant, which provides marginal quality improvements at the cost of increased computation and bitrate, confirming that the original Ghost convolution offers the best efficiency-accuracy trade-off. These results demonstrate that Ghost-NeRV provides an effective and practical solution for neural video representation, enabling substantial computational savings while maintaining high perceptual quality, making it well-suited for deployment in resource-constrained video compression scenarios.