Image Code Embedding via Adaptive Block Segmentation
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Solution Overview
Problem
Existing methods for embedding codes into large image data often result in significant image quality deterioration, as the size of the image increases, exceeding the allowable range of human perception.
Innovation Solution
The method involves dividing original image data into blocks, calculating the size of embedding blocks based on image size, and embedding codes by operating on the characteristics of pairs of blocks, thereby minimizing image deterioration by adjusting the embedding block size according to the original image data's resolution and embedding size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If code embedding is performed on large image data using conventional methods, then information can be embedded into the image, but image quality deteriorates significantly
Solution Approach 1:
The image is divided into multiple blocks, and code embedding is performed on selected blocks rather than the entire image. This segmentation approach allows information to be embedded while limiting the impact on overall image quality by concentrating modifications on specific regions.
Solution Approach 2:
The invention selectively applies code embedding to specific blocks based on their characteristics (e.g., blocks with higher tolerance for modification). By adapting the embedding process to local block properties rather than applying uniform processing across the entire image, quality deterioration is minimized in regions where it matters most.
2Productivity
If the embedding block size is increased to embed more information, then embedding efficiency improves, but image deterioration increases
Solution Approach 1:
The embedding block size is not fixed but dynamically adjusted based on image characteristics and embedding requirements. The system can adaptively select different block sizes for different regions or embedding scenarios, optimizing the balance between embedding efficiency and quality preservation.
Solution Approach 2:
The invention changes the parameter of block size adaptively rather than using a constant size. By adjusting block size parameters based on local image characteristics, the system achieves better embedding efficiency in suitable regions while maintaining quality in sensitive regions.
Data Source
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AI summary
According to an aspect of an embodiment, a method of embedding information into an image comprises dividing the image into a plurality of blocks, providing a plurality of embedded blocks corresponding to the plurality of blocks, respectively, each of the embedded blocks having the same size as each of the blocks when each of the blocks is smaller than a predetermined size, each of the embedded blocks having the predetermined size and being placed at the center of each the block when each of the blocks is not smaller than the predetermined size and selectively modifying the characteristic value of each of the embedded blocks in accordance with the information to be embedded.