Why we calculate histogram before and after embedding in the image processing ?

Question

Why we calculate histogram before and after embedding in the image processing ?

II. ALGORITHM We first use the "Lena" image as an example to illustrate our algorithm. Then the dada embedding and extracting of the pro- posed algorithm are presented in terms of pseudocode. Finally e important issues including data embedding capacity a addressed For a given grayscale image, say, the Lena image (512 × 512 x 8), we first generate its histogram as shown in Fig. 1. A. Illustration of Embedding Algorithm Using an Example With One Zero Point and One Peak Point 1) In the histogram, we first find a zero point, and then a peak point. A zero point corresponds to the grayscale value which no pixel in the given image assumes, e.g., h(255) as shown in Fig.. A peak point corresponds to the grayscale value which the maximum number of pixels in the given image assumes, e.g., h(154) as shown in Fig. 1. For the sake of notational simplicity, only one zero point

Explanation / Answer

Histogram depicts the distribution of pixel intensity in an image. X-axis represents the gray level intensity and Y-axis represents the no. of pixels of respective gray level intensity. For a high contrast image, the histogram show a U-shape image while a flatish histogram show an enhanced image. Histogram helps in deciding a threshold value in image binarization algorithm. If an object with known gray level intensity is embedded in an image, the shifting of histogram peak helps in image analysis process. Histogram also helps in computing the image entropy i.e. information content in an image.

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