bit depth

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A description of the number of colours or shades of gray a monitor can display or a scanner can process; the higher the bit depth, the higher the number of colour hues that can be supported
Segen's Medical Dictionary. © 2012 Farlex, Inc. All rights reserved.

bit depth

The number of shades of gray in a digital image. Bit depth is defined as 2n , where the multiplier 2 indicates that the image is in black and white and n represents the power to which the computer can multiply this function. Therefore a bit depth of 21 would produce 2 shades of gray; a bit depth of 212 would produce 4096 shades of gray. Digital radiography, CT, and MR images are produced with bit depths of 10 or 12.
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In order to ensure the relationship of zero tree of bit plane, we use a first-order linear predictor to compute the coefficients as
Source X Bit planes of frame 2 of grandma video sequence.
So when the last 6 bits are kept in the range from 000000 to 111111 although some noise added, the second significant bit plane replaced with secret image will not be affected, and the secret image can still be rebuilt.
In the proposed technique, all the images are QR codes, which have their own abilities to correct errors, and the plane modification is the second significant bit plane of the grayscale image, in this way, the algorithm is robust to conventional image attacks.
Since all the images are QR codes, which have their own abilities to correct errors, and the plane modification is the second significant bit plane of the grayscale image, our scheme is robust to conventional image attacks, such as rotation, JPEG compression, Gaussian noise, resizing and cropping, when reconstructing the secret image, performing XOR operation on the bits of the codewords region in the second significant planes of the grayscale shares and adding the functional patterns, version, and format information together.
Then the n temporary binary QR code images are substituted for the second significant bit planes of the grayscale cover QR code images to output n shares.
The secret QR code image can be recovered by extracting the secondary significant bit planes of n gray QR code images to generate n binary QR code images firstly and then XORing white and dark modules in the codewords region of the n binary images, adding the functional patterns, version, and format information later.
When sharing the secret QR code image into arbitrary binary QR code images, we only encrypt the codewords region while other parts are identical with the binary QR code images, then replace the second significant bit planes of the grayscale QR code cover images with the encrypted binary QR code images.
Modifying the codewords of temporary QR codes according to steps (4)-(10) in Algorithm 1 to generate n binary QR code images and then replace the second significant bit planes of the grayscale QR code cover images with the binary QR code images to generate n shares.
Where [c.sub.i,j] is the wavelet transformed coefficient value at (i,j) in space and Significant state of set t at bit plane n is [S.sub.n](t)
Hence, for watermarking, ciphered bytes from most significant bit planes degrades the image quality to a greater extent so we choose inserting watermark in the ciphered bytes from the less significant bit planes of the middle resolutions.