Automated Marsh-like classification of celiac disease in children using local texture operators
Computers in Biology and Medicine
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This paper proposes a blind image watermarking scheme using wavelet trees quantization. The largest two coefficients in a wavelet tree are called significant coefficients and the difference between them is called significant difference. We embed a watermark bit by comparing the significant difference with an average significant difference value, and the maximum coefficients are quantized. So there exists a large magnitude difference between the maximum coefficients and the second maximum coefficients after watermark embedding, which can be used for blind extracting watermark. As a result, the watermarked image looks lossless comparing with the original image. Moreover, by designing an adaptive threshold in the extracting process, our method can effectively resist common image processing attacks such as JPEG compression, media filtering, Gaussian noise, cropping, sharpening, histogram equalization and so on, and it has a good transparency.