HDR Image Watermarking based on Saliency Detection and Quantization Index Modulation

High-dynamic range (HDR) images are circulated rapidly over the internet with risks of being exploited for unauthorized usage. To protect these images, some HDR image-based watermarking methods were put forward. However, they inherited the same problem faced by the conventional IW methods for standard dynamic range images, where only trade-offs among conflicting requirements are managed instead of simultaneous improvement. In this paper, a novel saliency (eye-catching object) detection based trade-off independent HDR-IW is proposed, to simultaneously improve robustness, imperceptibility and payload. First, the host image goes through our proposed salient object detection model to produce a saliency map, which is, in turn, exploited to segment the foreground and background of the host image. Therefore, a convolutional deep learning model is trained with various datasets containing multiple salient objects. Next, the binary watermark is partitioned into the foregrounds and backgrounds using the same mask and scrambled using a random permutation algorithm. Finally, the watermark segments are embedded into the selected bit-plane of the corresponding host segments using quantized indexed modulation. Experimental results suggest that the proposed work outperforms state-of-the-art methods in terms of concurrently improving the aforementioned conflicting requirements.