# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results  
### Configuration  
To validate the robustness, imperceptibility, and computational efficiency of the proposed watermarking scheme, we conducted a comprehensive set of experiments using a diverse set of publicly available video sequences [42], covering three commonly used reso  
Figure 2: Summary of the complete embedding and extraction watermarking process. lutions and visual contexts. The CIF set (352 \(\times\) 288) includes Akiyo, Carphone, Foreman, Grandma, Hall Monitor, Suzie, and Tempete; the 4 CIF set (704 \(\times\) 576) includes Australia, City, Crew, Harbour, Ice, Football, and Soccer; and the HD set (1280 \(\times\) 720) includes Snow Mountain, Ducks Take Off, Kristen and Sara, Speed Bag, Tractor, and Vidyo1. These sequences span indoor and outdoor settings, low and high motion dynamics, and varying textures such as faces, objects, and natural backgrounds. All videos were between 8 and 10 s in duration, encoded at either 25 or 30 frames per second, and processed in their uncompressed YUV 4:2:0 format. To test robustness under realistic degradation scenarios, we simulated a range of common signal and geometric attacks. Compression was applied using H.264 (CRF = 28) and VP8 (Q = 30) encoding. Gaussian noise was introduced with \(\mu=0\) and \(\sigma^{2}=0.05\). Geometric transformations included small-angle rotations (\(\pm 5^{\circ}\)), rescaling (90%), and cropping of 10% from each border (top, bottom, left, and right). Temporal desynchronization was performed by shifting watermark extraction indices randomly by \(\pm 3\) frames. These conditions were chosen to reflect the most common distortions encountered in multimedia transmission, compression pipelines, and adversarial tampering. The watermark used in all experiments consisted of 128-bit pseudo-random binary sequences. Each bit \(w_{i}\), was embedded across \(r=5\) keyframes identified through histogram-based scene segmentation. Embedding was achieved on the \(C(2,1)\) DCT coefficient of \(8\times 8\) blocks, computed directly in the spatial domain as described in Section 3.1.3. The quantization step \(\Delta\) was modulated using the local JND threshold and a global scaling factor. The JND values incorporated luminance, spatial, and contrast masking terms. Table 1 presents the parameters together with their corresponding values utilized in all experiments. The selection of hyperparameters for the proposed method was guided by empirical testing and insights from the prior literature. The weights \(\alpha\), \(\beta\), and \(\gamma\) in the perceptual score map were set to 0.4, 0.3, and 0.3, respectively, following a grid search on a subset of CIF and 4 CIF videos to balance between saliency suppression and detail enhancement. The quantization step scaling factor \(Q\) was selected to match the perceptual thresholding behavior of prior JND-based video watermarking studies [18], offering a trade-off between imperceptibility and bit recoverability. The redundancy factor \(r\) was chosen based on results in Section 4.2.2, which showed that performance gains saturated at this level. The scene-change threshold \(\theta=30\) (used in histogram-based keyframe detection) was selected empirically to reliably identify transitions across diverse content types. All parameters were kept fixed across all test sequences to ensure consistency and comparability. To assess the performance of the proposed watermarking scheme, we employed a set of widely used evaluation metrics that capture different aspects of quality and robustness. Peak Signal-to-Noise Ratio (PSNR) was used to quantify signal-level distortion, while Structural Similarity Index (SSIM) [43] evaluated the preservation of structural fidelity in the watermarked video. To estimate perceptual visual quality, we applied Video Multimethod Assessment Fusion (VMAF) [44], which uses machine learning models trained on subjective