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Abstract
This system is proposed for digital images, mainly focussed on grey scale images. Usual algorithms just keep the PSNR values high to enhance the contrast of an image, where this system enhances the image quality as well as its security and decreases the bandwidth consumed. For embedding data in the image highest two peaks in the histogram are selected and then repeated the same process for performing histogram equalization. The data is embedded in the image, compressed and encrypted using chaotic encryption such that the data and image are completely recoverable. The system with the proposed algorithm was performed on two sets of images to prove its efficiency. It is found that, by embedding considerable amount of data into the image, the contrast of the image is being enhanced. It is proved that, this system works better than the three inbuilt MATLAB functions for contrast enhancement which as an add-on provides compression and encryption for better security.
Introduction Reversible Data Hiding (RDH) also referred to as Lossless data hiding, or invertible data hiding is used widely in the field of signal processing. RDH is usually used to hide a piece
of data into an image to produce a marked image. The highlight of RDH is that it gives back the original image after the data is being extracted from the marked image. This manner of RDH is useful in most vulnerable applications where no permanent change is helped about the host signal . In the literary works, the majority of the planned algorithms tend to be for digital photos to introduce undetectable information (e.g. [1]–[8]) or maybe a seen watermark.(e.g [9]).
Inorder to evaluate the performance of the RDH algorithm we usually use hiding rate and marked image quality as the two important metrics. It is often identified that the distortion of the image is proportionate to the hiding rate. Inorder to measure this distortion the Peak Signal to Noise Ratio (PSNR) value of the marked image is used. In general, if the image histogram [2] is modified directly, it affects the embedding capacity adversely. Even if the PSNR value is improved by using prediction error based algorithms, due to the distortion introduced in the image during the embedding operations hardly improve the visual quality. With the images with inadequate light, increasing the particular visible excellent is a lot more essential as compared to preserving the particular PSNR value high.
The security of the data being transmitted across a network has become important with the increase of internet and other communication methods. The most common method used is hiding data in a cover media and sending it. The cover media used here in RDH is an image. It can be text, image, audio or video. After the data is embedded in the cover image, the embedded message is compressed using the Huffman‟s coding which is further encrypted using the chaotic encryption technique which uses three keys. To send, store and receive the data efficiently a compression algorithm helps to a large extend. Once compression is done, encryption is the process which makes the data embedded and compressed image secured for being transmitted over the network. To our best knowledge this is the first system that implements the compressed and highly secured RDH algorithm with contrast enhancement.
In this system, contrast enhancement and data embedding are done simultaneously, where the contrast enhancement is obtained by equalising the histogram[10] which modifies the histogram of pixel values. At first, the highest two peaks of the histogram is selected and keeping the bins in between the peaks unchanged the outer bins are shifted towards outward. And the bins are splitted into two adjacent bins. The highest bins are further chosen in the modified histogram and they are split. This process is repeated until the required contrast enhancement is obtained. There may occur some overflow or underflow conditions which is eliminated by pre-processing the image and generating a location map. This location map is attached with the image so that it can be used to extract the data effectively at the receiver side. Thus pre-processed and contrast enhanced image is then compressed using a Huffman coding technique which employs the use of a binary tree. The compressed image is then selected for encryption using a chaotic encryption technique
which uses three encryption keys for the operation. The system is implemented in two sets of images which prove that it not only increases the contrast, ie, image quality as well as improves the sending quality by compression and security by encryption.
The rest of the paper is arranged as follows: Section II describes the proposed algorithm for RDH with contrast enhancement, Compression and Encryption. Section III
shows the experimental results and Section IV gives the conclusion drawn from the system.
2. RDH Algorithm with Contrast Enhancement followed By Compression and
Encryption
A. Embedding data with Histogram Modification
The proposed system is focused primarily on gray scale images. The image histogram is calculated by counting the number of pixels with a gray level value, j, in a given 8-bit gray-level image where j {0,1,2,….254, 255}. hI (j) denotes the number of pixels in the image with a pixel value j, where hI denotes the image histogram. Assume I consists of N various pixel values. The highest two bins are chosen from the N non empty bins in hI. The smaller bin is represented as IS and the higher bin is represented as IR. The data embedding for a particular pixel with value i in hI is:
i -1 , for i < IS
IS - bk , for i = IS
i‟‟ = i , for IS < i < IR (1) IR + bk, for i = IR
i + 1, for i > IR
i‟ represents the modified pixel value and bk represents the k-th bit of the message which can be either 0 or 1. The Eq. (1) is applied to each pixel in hI such that the bins between the peaks remain unchanged and the outer peaks are moved outward. Thus they can be split two bins ie, IS -1, and IS , IR , IR+1.
To retain the the original IS and IR values, 16 pixels in I are excluded from histogram computing and the LSB of those pixels are included in the binary values to be hidden. While retrieving data from the image, the peak values are retrieved and the histogram of the marked image excluding the 16 pixels is calculated. Then to obtain the data bits, Eq (2) is applied to it :
1, if i‟ = IS -1
b‟k = 0, if i‟ = IS (2)
0, if i‟ = IR
1, if i‟ = IR+1
b‟k represents the k-th data bit extracted from the marked image I‟.The reverse of Eq. (1) is applied
on the image to obtain its original values:
B. Pre – Process to avoid Overflow and Underflow
All the pixel values are necessarily to be within the range {1……254}. If any of the pixel value is out of this range (0 or 255) then overflow or underflow condition will occur . To avoid such error conditions, the image is preprocessed before its histogram is being modified. In pre processing step, the pixels with value 0 is added by 1 and pixels with value 255 are subtracted by 1 which will
avoid the overflow and underflow condition since while shifting histogram, the possible change for a
pixel value is
1 . While extracting the data, the original value of these pixels is needed and hence
to memorize those values, a location map is generated which has the same size as that of the original image. The location map assigns a value 1 to the location of modified pixels whereas a value 0 to the location of unchanged pixels. The location map is pre-computed and added to binary values which are to be hidden in the image. While recovery operations, the location map is extracted and the changed pixels are given its original values and then the data is extracted as well as the image is recovered completely.
C. Enhancing the Contrast
To increase the rate of hiding of the data in the image, the Eq. (1) is repeated several times on the image such that the peaks are obtained each time an operation is performed and the Eq (1) is applied to the peaks obtained from the modified histogram. This process helps in achieving Histogram equalization which increases the contrast of the image. Hence by applying such an operation Histogram equalization and contrast enhancement are obtained at the same time. Let the level of equalization be L , ie, the equation is applied L times, then overflow underflow conditions may occur. To avoid this, while preprocessing, all the pixel values from 0 to L – 1 are added by L and all the pixel values from 256 – L to 255 are subtracted with L and the corresponding location map is generated by adding 0s and 1s at the appropriate positions. While extraction the location map is extracted and the pixel values are replaced with its original values.
D. Huffman’s Encoding
Compression and decompression are the processes done to make efficient, the sending
,storing and receiving of the data. The preprocessed , data embedded image is compressed before it is subjected to encryption operation. The compression here is obtained by means of Huffman‟s encoding algorithm which uses binary trees for the purpose.
In an image, each of the pixel is represented using 8 bits. Huffman encoding technique compressed the image by reducing the number of pixels used to represent a bit. The technique applies lesser number of bits to the most often appearing pixels and more number of bits to the less often appearing pixels. Thus, the size of the image thus obtained will be lesser than that of the original image.