# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 5 Analysis and Discussion
### Comparison with State-of-the-Art Methods  
To contextualize the performance of the proposed video watermarking method within the current state-of-the-art, we conducted a comparative analysis against five recent and representative watermarking schemes reported in the literature. These include both classical signal processing-based techniques and deep learning-based (DL) approaches, enabling a balanced assessment of robustness, quality, and computational cost.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Stage** & **Avg. Time (CIF)** & **Avg. Time (HD)** & **\% Total Time** & **Parallelizable** \\ \hline Perceptual Map & 1.6 s & 4.3 s & 45\% & Yes \\ Keyframe Detection + Redundancy & 0.5 s & 1.2 s & 15\% & Partial \\ QIM Embedding (Spatial-DCT) & 0.4 s & 0.9 s & 10\% & Yes \\ Extraction (sync + voting) & 0.7 s & 1.6 s & 30\% & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational Cost Breakdown.  
In particular, we selected two DL-based methods that have gained attention in recent years: DvMark [23], which uses a multiscale frequency-domain neural network trained to embed and extract watermarks robustly under compression; and Hide-and-Track [24], a blind deep-watermarking system trained with adversarial desynchronization objectives. Both of these methods require dedicated training on video datasets and rely on GPU-accelerated inference for watermark extraction.  
These were compared alongside traditional transform-domain and hybrid schemes, including [PERSON] et al. [19] (DT-CWT-based blind embedding), [PERSON] et al. [17] (hybrid watermarking with transcoding resistance), and [PERSON] et al. [25] (a spatial-domain method with DCT estimation). The proposed method distinguishes itself by not relying on any training phase, operating entirely in the spatial domain, and requiring no access to the original video at extraction time.  
As shown in Table 9, the proposed method offers superior imperceptibility and robustness while maintaining lower computational cost compared to deep learning-based and transform-domain schemes. Unlike frequency-domain neural approaches, which often require GPU acceleration and long training times, our system achieves comparable or better BER and quality metrics using a lightweight perceptual framework and simple QIM encoding. Compared to our previous method [25], this new version introduces temporal redundancy and enhanced JND modulation, substantially improving BER performance while preserving visual quality  
These findings highlight that integrating perceptual saliency, spatial-domain coefficient estimation, and scene-aware multiframe embedding can outperform more complex models without sacrificing execution time or generalizability.  
It is important to note that while our evaluation included a diverse set of video sequences with varying resolutions and motion dynamics (as detailed in Section 4.1.), the datasets were not focused on mobile or real-time streaming scenarios. Future work will address such use cases.  
Furthermore, the comparative results shown in Table 9 were either reproduced using publicly available code from the original authors or extracted from their published  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
* CIF, 4 CIF, and HD video averages under Gaussian noise, H.264 compression, and cropping.  
\end{table}
Table 9: Comparison with existing video watermarking methods under common attacks 1.  
experimental sections. However, we acknowledge that not all methods were executed under identical hardware or software environments. To ensure fairness, our comparison is focused solely on functional metrics such as robustness (BER), imperceptibility (PSNR, SSIM), and watermark size.