In BPCS, a multi-valued image (P) consisting of n-**bit** pixels can be decomposed into set of n – binary pictures. Ordinary image data is represented by a pure binary code system which is commonly used in image processing. However CGC is preferred over PBC in BPCS steganography. Example: P is an n-**bit** gray image say n=8. Therefore P = [P7 P6 P5 P4 P3 P2 P1 P0] where P7 is the MSB **bit** **plane** and P0 is the LSB **bit** **plane**. Each **bit** **plane** can be segmented into “informative” and “noise” region. An informative region consists of simple pattern while noise-like region consists of complex pattern. In BPCS, we replace each noise-looking region with another noise-looking pattern without changing the overall image quality. Thus, BPCS steganography makes use of this nature of human vision system [9] [10].

Many watermarking algorithms have been proposed in last few years that ensure the security of the watermark signal and authenticity of the host message signal. The work presented in [8] by Prof. Jeebananda Panda et. al. is the Least Significant **Bit** (LSB) substitution method of audio watermarking in time-domain single **bit** **plane**. The watermark along with synchronisation bits is embedded in the 1 st LSB of all the audio samples and the attained results are compared when the algorithm is recurred for 2 nd , 3 rd and 4 th LSB. The watermarked signal is subjected to various attacks and by computing similarity between original and recovered watermark and SNR of audio signal, it is observed that algorithm is more robust corresponding to 3 rd and 4 th LSB substitution. Deepshikha Chopra et. al in [6] presented an invisible watermarking technique (lower LSB e.g. 1 st ) and a visible watermarking technique (higher LSB e.g. 7 th ) for image. The robustness of each technique is studied by Peak signal to noise ratio (PSNR) and mean square error (MSE). In [1], Ali Al-Haj et. al. presented an algorithm for audio watermarking in frequency-domain where the watermark image is embedded at LSB of the coefficients of transformed host audio signal. The transformed signal is obtain using Discrete Wavelet Transform (DWT). The proposed algorithm performed better than most traditional techniques. Prof. Samir Kumar in [5] worked on Audio Steganography techniques using LSB modification, phase encoding, parity coding and Spread Spectrum. In LSB coding technique, the least significant **bit** is modified to embed data. In phase encoding scheme, the phase of carrier file is replaced with reference phase which represents hidden data. In parity coding, signals are divided into regions and then parity **bit** of each region is calculated and matched with secret message **bit**

The **bit** **plane** slicing is applied on MRI image of AD affected patient and then canny edge detection operator is applied on each **bit** **plane**. It is very difficult to find inner and outer boundaries of AD images and the edge features in zeroth and first **bit**- plane.Canny Method on Zero and First **Bit** Plan applied on canny operator to find edges. It detects wide range of edges in AD images. Some algorithms based on the result of edge detection and edge detection algorithms are not sufficient an image. [3] Canny edge detection is used to generate the edge map image. The large intensity gradients are more likely to correspond to small intensity gradients. From the result of second and third **bit** **plane** with canny, it is difficult to find inner, outer boundaries and edges correctly. The lower **bit** planes are completely black. But fifth, sixth and seventh **bit** planes show inner and outer boundaries of AD images and recognizes edges correctly. (See Figure 7) When compared, it has been observed that the sixth and seventh **bit** **plane** shows few edges than that of fifth **bit** **plane**. From Sixth **bit** **plane** more, edge features are recognized as it contains the majority of visually significant data. Below figure shows the **bit** **plane** slice from zero **bit** to seventh **bit** applied to the MRI images.

As the retinal image has very small variation in intensity throughout the image with little **bit** contrast around the optic disk region, it is not an easy task to segment the optic disk using simple threshold techniques. So, accurate segmentation of OD requires multi-level of segmentation process. As the first step, the region of interest is segmented from the input fundus image using **bit** **plane** slicing method. Any gray scale image can be split into equivalent binary planes called **bit** planes. **Bit**-**plane** slicing is the process of extracting the specific **bit** **plane** that contributes to the region of interest in which optic disk presents. Separating a digital image into its bits planes is useful analyzing the relative importance played by each **bit** of the image, a process that aids in determining the adequacy of number of bits used to quantize each pixel (Gonzalez and Woods, [16]). Suppose, if a digital image is considered as composition of eight 1-**bit** planes, ranging from 0 for least significant **bit** to **plane** 7 for most significant **bit**. In terms of 8-**bit** bytes, **plane** 0 contains all the lowest order bits in the bytes comprising the pixel in the image and **plane** 7 contains all the higher order bits. Figure-2 illustrates these ideas. Note that higher order bits (especially top few planes) contains the majority of visually significant data whereas the other **bit** planes contribute to more subtle details in the image.

ABSTRACT: The importance of the image analysis with respect to industrial, medical, satellite image processing applications is gaining attention of many researchers in recent times. The recognition of faults present in the damaged images is vital for based applications. In this paper, we aim at developing a method for identifying faults that present in images. Our approach is based on the concept of **Bit** **Plane** Filter using convex hull methods. The **bit** **plane** filtering methods used to slice the given images to fix on the affected portion of the given images. The convex hull method is used to identify the control points that are needed for reconstruction of images. The performance of **bit** **plane** method is evaluated using simulation and it is proved that our approach produces better results when compared to current methods.

Image encryption plays a major role in information security. It is mainly used to convert the original image into another form. In this work, we propose a **bit** **plane** slicing of digital image to provide the more security. To enhance security of the bitplane decomposition based image encryption methods, this paper introd uces a novel image encryption algorithm using a bitplane of a source image as the security key bitplane to encrypt images. It focuses on three techniques such as image scrambling, **bit** **plane** slicing and image rotation for efficient image encryption. Arnold scrambling and **bit** **plane** slicing process are performed in the source image. From the decomposed source image, particular bitplane is assigned as the security key **bit** **plane** to perform the encryption process in the original image. As an example, this paper also proposes a **bit**- level scrambling algorithm to change **bit** positions. Simulations and security analysis are provided to demonstrate an excellent encryption performance of the proposed algorithm.

Visual Cryptography Scheme (VCS) is an encryption method that works on human visual system. It encrypts a secret image into n shares and decryption can be done only by stacking k or more share images without any computation. A new secret sharing scheme with meaningful shares using (k,n)-threshold visual cryptography and digital watermarking for grayscale images based on **bit** **plane** encoding is proposed in this paper, that encrypts a grayscale secret image in such a way that results of encryption is in the form of shares. Shares do not reflect any information directly, information is scrambled instead. Firstly, an image is decomposed into its **bit** **plane** images that generate a binary image at each **bit** **plane**. Secondly, the traditional binary secret sharing scheme is used to get the sharing images. Finally, a proposed watermarking technique is used to generate meaningful shares. To decrypt hidden secret image, extract the shares from the cover image and decompose each share into **bit** planes and then secret grayscale image is reconstructed. This scheme provides a more efficient way to hide images in different meaningful shares. Furthermore, the size of the hidden secret can be recovered by inspecting the blocks in the shares.

Texture understanding and analysis play important roles in many aspects of computer vision and image processing. Despite lots of efforts in last decades, it still remains a challenging problem to model textures efficiently. Some available methods of texture classification and retrieval can be found in [1,2,3,4]. SVD is an important matrix theory and has been popularly employed in image processing, such as data compression [5], texture segmentation [6], and texture classification [7]. Specifically, wavelet-based methods have been intensively researched since wavelet analysis offers an efficient representation of multiresolutions and orientations of images [8,9,10,11]. Recently, models based on wavelet subband coefficients have also been used on texture classification. The existing models in literatures contain the Characteristic Generalized Gaussian Density (CGGD) model [12], the **Bit**-**plane** Probability (BP) model [13,14], the Refined Histogram [15], the Local Energy Histogram [16], and so on. Particularly, the **Bit**-**plane** Probability (BP) signature is a very competitive feature by modeling wavelet high-frequency subband coefficients via the Product Bernoulli Distributions (PBD). The **Bit**-**plane** Probability (BP) signature is built by the PBD model parameters. More specifically, the BP signature in conjunction with the use of weighted L -norm distance and the minimum distance classifier is presented concretely in paper [14].

An 8 **bit** binary vector can be used to denote the intensity value of each pixel and the value of each **bit** is either 0 or 1. Each **bit** **plane** can be represented as a binary matrix. [15,16]. This binary matrix is used further to generate image slices for the respective **bit** planes. The figure 1 shows the original image along with image slices for higher **bit** planes, also the image slice generated by accumulation of higher **bit** planes is shown. The **bit** **plane** of the image can be given as equation 1 where the original image is given as I(m,n), R is given as Remainder and floor(i) stands for round the elements to I nearest integers less than or equal to i.

current frame is matched with the previous frame [11]. The amount of computation required by FS-FM or FS-BM using MAD or MSE is very large as reported in [1, 3, 4]. Hence, var- ious algorithms have been proposed to reduce the computa- tion for motion estimation [1, 3–6, 10–14]. One approach to reduce the computation is to reduce the size of blocks for which LMVs are derived [5, 10]. In this approach, small blocks in the four corners of an image frame are, in general, chosen for the derivation of LMVs and the GMV is derived based on these four LMVs. Note that there is a high possi- bility that the corners of an image is a background area of which the motion should be compensated by DIS while the movement of foreground objects should be preserved even with DIS. Another approach for computation reduction is to reduce the number of pixels in a block for motion esti- mation. In [6], the edge pattern of an image is derived and only edge regions are compared for the best match between frames. This method reduces the computation at the expense of the accuracy of motion estimation. Fast motion estimation methods based on **bit**-**plane** or gray-coded **bit**-**plane** match- ing have been proposed in [3, 4], respectively. These ap- proaches reduce the number of bits to represent one pixel, re- sulting in the reduction of the computation while maintain- ing the motion estimation accuracy. Another method that obtains the motion vector using a binary operation is one-**bit** transform (1BT)-based motion estimation in which image frames are transformed into a single **bit**-**plane** after compar- ing the original image frame against its multiband-pass fil- tered version [10]. This method also provides a low compu- tational complexity with reasonably accurate motion estima- tion results. Subimage phase-correlation-based global mo- tion estimation is proposed in [1, 15]. This algorithm gen- erates relatively accurate motion estimation results, but re- quires large computational complexity due to the computa- tion for 2D-Fourier transforms [10]. Recently, a digital image stabilizer integrated with a video codec has been proposed in [16]. One of the three schemes proposed in this research is a technique to reduce the computational complexity of the digital stabilizer by using the information obtained by the motion estimation in the video codec.

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In this paper, we introduced a novel encryption algorithm based on Fibonacci numbers. In addition, novel **bit**-**plane** decomposition for Fibonacci weights was also discussed that offers cryptographic benefits to the proposed system. This lossless image encryption algorithm can employed for privacy protection and could address both pay-per-view applications or secured communication simultaneously. Simulation results and analysis verified that the algorithm shows good performance in image encryption.

Here, we have computed the PSNR values and MSE for different LSB **Bit** Substitutions and we found that if we increase the number of LSB **Bit** Substitutions from 1 to 5, PSNR value will increase and the MSE value will decrease. As we know PSNR value is inversely proportional to MSE, so with decrease in MSE will make sure that PSNR value should decrease. In this paper, we have kept on substituting the LSB bits up to 5 since after that Stego Image will no longer look like the cover image.

A new fragile watermarking algorithm for medical images is proposed. This algorithm makes it possible to resolve the security and forgery problem of the medical images. Instead of the discrete wavelet transform, an integer wavelet transform is used to utilize hash function. The watermark associated with the hash values is inserted into the LSBs of the integer wavelet transform coefficients. This algorithm also detects a forged area of the image very well. The conventional fragile watermarking methods have handled a watermark for image; Most of them insert the watermark into the LSBs of cover image. In this case, they have a problem that the watermark can be removed easily by modifying LSBs. To overcome such a problem, a new fragile watermarking algorithm using selective **bit** **plane** mechanism is proposed .The proposed algorithm uses **bit** **plane** schema in integer wavelet transform domain and solves the LSB inserting problem, which is used in conventional fragile watermarking techniques. In this technique, the inserted **bit** **plane** is selected randomly with a key. It also utilizes integer wavelet transform domain instead of spatial one. One advantage of using an integer wavelet transform is that the transformed coefficients are integers, which can be used in a hash function.

**Bit**-**plane** Decoding: **Bit** **plane** coding generate a binary pattern called as local **bit**-**plane** decoded pattern. This is generated by exploring the relation between of a centre pixel with the local **bit**-**plane** transformed values for each **bit**-**plane**. The intensity values of pixels are used to explore or to identify the relationship between the pixels of an image. The decomposed and transformed values are combined together to obtain a single value with which new feature descriptor is calculated. Here signed function is applied to the difference of intensity of centre pixel and local **bit** **plane** transformed values[2].

The input message is broken up into square blocks of 8x8 pixels. If the data to embed (8x8 blocks at a time) in the cover file is found to be complex, it can be directly embedded into the complex blocks of the cover image [4] [5]. If not, we would conjugate (exclusive-or) the data with a white checkerboard pattern (the most complex pattern) to ensure minimum complexity [8]. We will need a conjugation **bit** in each **plane** to indicate whether the data is conjugated with a checkerboard or not. This uses up 1 **bit** of embedding space per 8x8 region leaving 63 bits to embed per 8x8 **bit** **plane** [3] [8]. Once the data has been embedded, image is converted into the PNG format (Portable Network Graphics) and saved on to disk. The PNG compression algorithm is among the best in providing lossless compression. Unlike JPEG format, PNG compression involves no loss of data. Use of PNG format is indispensable for our application because any lossy compression of output image will lead to significant data-loss [8].

This paper acquaints us with a novel scheme of separable reversible encrypted data hiding into encrypted image using Advanced Encryption Standards (AES)[1] and **Bit** **Plane** Complexity segmentation [2].In our proposed system, on sender side we are encrypting confidential data using AES algorithm to obtain a final encrypted d1ata then we select an image which acts as a cover image in which confidential data is to be hidden[3]. After selecting the image, the next step is to extract the features from the image and then we apply the BPCS algorithm which has a reputation of having high payload capacity[4] to embed encrypted data into the carrier image[5].Then AES algorithm is applied on the stego image so as to obtain an encrypted stego- image. This stego encrypted image is watermarked to protect the integrity of confidential data. This entire mechanism takes place on sender side and then this encrypted stego-image is sent to the receiver. In this model, uses symmetric cryptography. After sending the encrypted image by email we send the keys to the receiver by means of sms or call. The sending of keys via sms or call on mobile results into separation of channel which is a merit of our system because this helps us to increase the security of the system and it results in making the system highly secure.

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In this paper, we propose a novel **bit** **plane** slicing based lossy compression technique which takes much less storage space than jpeg compression. Given an image in JPEG format, we first convert it into gray level image (if the image is color one) followed by finding its **bit** **plane** images for **bit** 0 to **bit** 7, discard those bits for which the **bit** **plane** images don’t give so much information by dividing the pixel intensities by a factor of 2 j and thereby store the

In this paper, the X-Ray image is colorized by using RGB Color map. Then the **Bit**-**Plane** level 6 for the Original and the Color mapped image is computed. PSNR and MSE values for these Images are calculated and compared. Results shows that the MSE value for **Bit** **plane** 6 of Original and Color mapped images are very high. This concludes that the feature extraction in Color mapped X-Ray images yields more details than the gray scale X-Ray images.

So, for any combination of input1 or input2 this IC will add them and show the output with carry **bit**. In these way 28 operations has been set. Some operations have sub operations also. Our 12 th operation is Universal Shift Register. In order to use this IC we have to select 12 th operation at first means D4=0, D3=1, D2=1 & D0=0. Under Universal shift register there are also 4 modes. They are Serial In Parallel Out (SIPO), Serial In Serial Out (SISO), Parallel In Serial Out (PISO) and Parallel In Parallel Out (PIPO). To select SIPO we have to select A6 & A7 **bit** as 0. Then we have to select which type of shifting we want. If we want to shift left then we have to select A0=0, A1=1, A2=1.

Some of the image processing applications such as compression require to know the contributions of individual bits made to the total image. **Bit**-**plane** slicing is a method to slice the image into different planes known as **bit**-planes. In general, 8-**bit** pixels are processed and the 8-**bit** image is divided into 8-**bit** planes. The 0 th **plane** is