MPEG-4 AVC perceptual masking. Case study: Robust video watermarking

Since the pioneering studies of Peterson and Watson, the perceptual masking for raw image has been playing an always increasing role in image processing: compression, filtering, indexing, watermarking, fingerprinting are just examples of application fields exploiting the human visual system peculiarities in order to increase their practical impact. The present study establishes the first accurate perceptual masking model addressing the MPEG-4 AVC domain peculiarities. In other words, we computed the MPEG-4 AVC visibility thresholds, i.e. the maximum amount of additive noise which can imperceptibly affect the quantized values of the intra prediction errors. This new masking model has been tested against other masking models, under the watermarking framework. On the one hand, when imposing a prescribed robustness and a fixed data payload, significant gains in transparency were obtained: the PSNR is increased by 2dB while the Watson's DVQ (Digital Video Quality) is decreased by a half. On the other hand, when imposing a prescribed robustness and transparency, gains in data payload by about 5% are reached.