# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results
### Baseline Performance Evaluation
#### 4.2.2 Impact of Redundant Coding  
One of the main innovations in the proposed watermarking framework is the use of temporal redundancy to increase robustness against accidental bit loss and reduce the impact of localized frame degradation. In practical scenarios--such as low-bitrate compression, transmission jitter, or dropped frames--it is highly likely that a portion of the embedded watermark bits may be corrupted or lost. By repeating each watermark bit across multiple temporally separated keyframes and applying a majority voting scheme during extraction, the system can effectively filter out isolated errors and improve the overall reliability of recovery.  
To evaluate this mechanism, we conducted an experiment where a 128-bit watermark was embedded using five different levels of redundancy, \(r\in\{1,2,3,4,5\}\), where \(r\) denotes the number of keyframes per bit. The watermarking process was carried out on all videos from the test set using adaptive quantization, and no external attacks were applied. The results were measured in terms of average BER, number of frames used, and recovery accuracy.  
As shown in Table 3, the BER decreases as the redundancy factor increases. Even a moderate level of redundancy (\(r=3\)) brings the BER below 2.0%, while full redundancy (\(r=5\)) pushes recovery accuracy to nearly 99.7%. These results demonstrate that temporal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Fixed \(\Delta\)** & **Adaptive \(\Delta\)** & **Improvement** \\ \hline PSNR (dB) & 39.42 & 50.12 & +10.70 \\ SSIM & 0.918 & 0.996 & +0.076 \\ VMAF & 83.1 & 97.3 & +14.2 \\ BER\% & 2.3 & 1.4 & \(-\)0.9 \\ \hline \hline \end{tabular}  
* Average across all videos.  
\end{table}
Table 2: Comparison of objective metrics with and without adaptive modulation 1.  
redundancy is a powerful mechanism for ensuring reliable watermark extraction, especially when used in conjunction with perceptually optimized embedding.  
To justify the selection of the default redundancy factor \(r=5\) in subsequent experiments, we carefully analyzed the trade-off between robustness and resource usage. As Table 3 indicates, increasing the redundancy factor consistently reduces BER and enhances recovery accuracy. The gain from \(r=4\) (1.1% BER, 98.9% accuracy) to \(r=5\) (0.2% BER, 99.8% accuracy) is still significant and yields nearly error-free extraction, making it a natural choice. Although using \(r=5\) implies 640 frames (compared to 128 when \(r=1\)), the cost is acceptable given the method's computational efficiency and the overall improvement in decoding reliability. We, therefore, adopted \(r=5\) as the standard configuration for all experiments involving distortion and benchmarking.  
While redundancy plays a key role in the robustness of the proposed method, it is important to clarify that the gains reported in Table 3 are primarily the result of temporal redundancy. In our setup, redundancy is implemented by distributing the same watermark bit across multiple keyframes in different temporal positions. Since all other components--such as block selection, embedding strength, and QIM modulation--remain fixed across these tests, the observed improvements in BER (from 5.2% to 0.2%) and recovery accuracy (from 94.8% to 99.8%) can be attributed directly to the redundancy mechanism. Moreover, because the watermark is embedded in different perceptually strong regions across frames, the impact of localized attacks (e.g., cropping or frame loss) is significantly mitigated. This confirms that temporal distribution is not only a complementary feature, but a critical contributor to the method's overall resilience.