Image digital watermarking algorithm system on basis of texture complexity and JND (just noticeable difference) models

Abstract

The invention discloses an image watermarking algorithm. Texture complexity and just noticeable difference (JND) models in discrete cosine transform (DCT) domains are combined with one another by theimage watermarking algorithm. The image watermarking algorithm includes segmenting original images into sub-blocks; computing the texture complexity of the sub-blocks by the aid of four texture features of a gray-level co-occurrence matrix of the various sub-blocks; sorting the sub-blocks on the basis of the texture complexity to determine locations of the sub-blocks with embedded watermarks and then carrying out DCT on pixel matrixes of the original images; computing JND values of various blocks by the aid of new partition modes; determining embedding modes for the watermarks in the sub-blocks according to the JND values and new embedding rules. The image watermarking algorithm has the advantages that texture features of image blocks and the vision sensitivity of human eyes are effectively considered, accordingly, the quality of images with the embedded watermarks can be improved, and the watermark embedding capacity can be expanded.

Description

technical field [0001] The invention relates to the field of image digital watermark algorithms, in particular to an image digital watermark algorithm system based on texture complexity and JND model. Background technique [0002] Texture feature analysis was originally applied to remote sensing image analysis. Since texture features are quantified image features in image calculations, effective macro and micro information of images can be obtained through their analysis, so texture feature analysis has become an important aspect of image processing, digital watermarking, etc. important research avenues. However, due to the limitations of its embedding rules, the embedding capacity of the watermark is limited. At the same time, the watermark is embedded on the most complex 128×128 sub-block of the image texture, which affects the image quality and robustness. [0003] Just Noticeable Difference (JND) describes the maximum image distortion that cannot be detected by the huma...

AI Technical Summary

Problems solved by technology

To sum up, the texture analysis based on Gary Level Co-occurrence Matrix (GLCM) and the transform domain JND model have extremely broad application prospects in watermark embedding, but there is no method to combine GLCM Combined with the DCT domain JND model to make up for the shortcomings of their respective models, so the present invention proposes a new watermark embedding method that combines the two

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