Large-capacity reversible image watermarking algorithm based on multi-scale decomposition and interpolation expansion

Abstract

The invention relates to the technical field of image processing, and discloses a large-capacity reversible image watermarking algorithm based on multi-scale decomposition and interpolation algorithm, including two parts: watermark embedding and watermark extraction, and the watermark is further enlarged by multi-scale decomposition and interpolation expansion The capacity of watermark embedding; the watermark information is embedded by the generalized difference extension method, and the overflow corresponding image pixels do not produce pixel value overflow through the grayscale overflow processing, and the embedded capacity is further increased, and the watermark is extracted as the watermark embedded reverse process. Compared with the prior art, the present invention maintains good visual quality of the watermarked image when embedding the same amount of watermark information, and has better invisibility and payload capacity.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a large-capacity reversible image watermarking algorithm based on multi-scale decomposition and interpolation expansion. Background technique [0002] Reversible image watermarking refers to embedding the watermark into the original image under the premise of ensuring the visual quality of the original image, and when the watermark is extracted, the original image can be restored without loss. Compared with traditional digital image watermarking, reversible image watermarking puts forward higher requirements for watermark embedding, and also makes it more widely used in judicial, military, medical and other fields that require high image authenticity and integrity. research and application value. One of the basic goals of reversible image watermarking algorithm research is to obtain the maximum amount of effective information embedding with less distortion. [0003] In...

AI Technical Summary

Problems solved by technology

However, there are also some shortcomings. In the case of dividing the original image into 4×4, there is still a lot of room for improvement in embedding capacity and reducing image distortion. In the case of embedding the same capacity of information, the visual quality needs to be improved.

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