Iterative self-adaptive reversible watermark embedding and extracting method
An iterative adaptive watermark embedding technology, applied in image data processing, instrumentation, image data processing, etc., can solve problems such as low visual quality, impact, and inability to embed all data
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2015-08-12
Smart Images
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Abstract
Description
technical field
[0001] The invention belongs to the cross field of image information security and digital image signal processing, relates to a reversible watermark embedding and extraction method, in particular to an iterative self-adaptive reversible watermark embedding and extraction method. Background technique
[0002] Reversible watermark refers to a special type of watermark that can be completely recovered after the watermark is extracted. Compared with traditional watermarking, reversible watermarking has strict requirements for lossless recovery of embedded carriers, and is generally used for distortion-free protection of important images, and has important application value in military images, medical images and remote sensing images.
[0003] The difference-extended reversible watermarking method proposed by Tian et al. is a typical method of image reversible watermarking. This method performs Haar integer wavelet transform on adjacent pixel pairs, and expands th...
Examples
Embodiment Construction
[0097] Principle of the present invention:
[0098] The embedding capacity of the existing reversible watermarking method and the visual quality of the carrier I′ after watermark embedding are closely related to the threshold T, but it is difficult to ensure that the embedding capacity and the visual quality of I′ are optimal at the same time when T is artificially selected. If T If the setting is too small, all the data cannot be embedded, and if it is too large, the visual quality of I′ will be low. At the same time τ=JBIG(L 0 ||L 1 ||L 2 ) is the compressed position map data, there is no necessary connection between T and len(τ), and the increase of T does not mean that the embedding capacity increases, so in the case of known β, it cannot be determined by the 2-point method T, and T itself is not necessarily an integer, so trying to T∈[0,49max(Var(s i ))] It is obviously unrealistic to perform violent enumeration in the interval. The essence of T is to determine all a...