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Let further w is the binary bit to be embedded.
Given that the bit to be embedded is, one index in the subblock will be chosen to change the value by if.
The latter algorithm, also known as ±1 embedding (see [1-3]), randomly increments or decrements a pixel or discrete cosine transformation (DCT) coefficient value to match the secret bit to be embedded when necessary.
If the bit to be embedded is "1", the coefficients in the odd description of the selected blocks will be enlarged while the coefficients in the even description of the corresponding block will be minimized.
In case, the bit to be embedded is "0", the embedded process is reversed, i.e., the coefficients in the odd descriptions are minimized and the ones in the even descriptions are enlarged.
Specifically, the image is divided into different blocks of random shapes, and the coefficients of each block are modified in order to make the block kurtosis equal to a non-uniformly quantized value determined by the bit to be embedded into the block.
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Step 2. For eight-bit plane, we extracted n-bit plane to generate (512 times 512) size CDMBP image while embedded bits E are generated randomly.
Let us consider a message m of M bits to be embedded in an HDR image.
First, the number of bits to be embedded in a certain coefficient is adaptive.
If the number of bits to be embedded is bigger than the number of 8 (times ) 8 blocks, we cannot embed each bit into each block.
It is worth mentioning that the number of bits that an image can carry is image dependent (i.e., it depends on the ridges area meaning that a larger area allows more bits to be embedded and vice versa).
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