Watermark embedding method and device based on content analysis and perceptual layering

Abstract

The invention provides a watermark embedding method based on content analysis. According to the method, basic element information and texture information are obtained by conducting initial schematic diagram decomposition on digital works; the wavelet coefficient of the basic element information and the wavelet coefficient of the texture information are obtained by conducting wavelet transform on the basic element information and the texture information respectively; the watermark intensity parameter values (PER or ENT) of the wavelet coefficient of the basic element information and the wavelet coefficient of texture information are calculated respectively, the two watermark intensity parameter values are combined so that a final watermark intensity parameter value can be obtained, and parameter embedding is conducted according to the final watermark intensity parameter value.

Description

technical field [0001] The invention relates to a watermark embedding method and device based on content analysis and perceptual layering. Background technique [0002] As a branch of information hiding technology, digital watermarking technology has natural advantages in providing effective technical means for digital product copyright protection. Use digital watermarking technology to hide specific copyright information in the original digital video content (such as TV programs), and make the original work high-definition under the premise of ensuring its invisibility (that is, satisfying low perception and not affecting the quality of the original work) , High-fidelity, after regular TV program release and signal transmission, it can still provide normal program viewing. Extracted only when necessary to provide appropriate proof of copyright. During the release and transmission of digital video content such as TV programs, the digital video content and the hidden digita...

AI Technical Summary

Problems solved by technology

However, most of the existing perceptual masking models or entropy masking models use global statistical properties based on pixels, so the calculated JND value cannot accurately reflect the visual perception of the human eye. For example, some areas in images / videos have high information entropy , but the human eye's attention to its content is very low. Such an information measure can easily mislead the embedding of the watermark and lead to a decline in robust performance and perceptual performance.

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