Bit Planes Of Image Research Articles

Image encryption combining discrete fractional angular transform with Arnold transform in image bit planes

Image encryption combining discrete fractional angular transform with Arnold transform in image bit planes

Secure data hiding scheme using shape generation algorithm: a key based approach

Data hiding in images has evolved as one of the trusted methods of secure data communication and numerous approaches have been introduced over the years using gray scale images as the cover media. Most of the methods are based on data hiding in least significant bit planes of cover images. Many such methods purely depend on data substitution algorithms by defining a pattern in which data is embedded. One can gain access to the secret data in a few attempts, if the algorithm is known. Keeping this in view several approaches based on secret keys have also been proposed by researchers. This paper proposes an efficient data embedding scheme using a key and an embedding pattern generated through midpoint circle generation algorithm. The pattern can be applied to a carrier that is mapped onto a grid/image. The cryptosystem uses the concept of steganography and is computationally light and secure. The secret-key is generated in such a way that Avalanche effect is ensured except in very rare cases. The proposed data embedding method is shown to be robust and highly secure while maintaining good hiding capacity and imperceptibility. It is applicable for data hiding in a generic grid that could be of pixels or bits.

A New Method of Image Steganography using Last Three Bit Plane of Gray Scale Images

A New Method of Image Steganography using Last Three Bit Plane of Gray Scale Images

Binary wavelet transform-based completed local binary pattern texture descriptors for classification of microscopic images of hardwood species

This paper presents an approach for generating a binary wavelet transform-based completed local binary pattern (BWTCLBP) texture descriptor to improve the classification accuracy of microscopic images of hardwood species. Firstly, gray-level slicing method is used to obtain eight (b0–b7) bit planes from grayscale image. Then, the two-dimensional binary wavelet transform (2D-BWT) decomposes each of the most significant bit-plane (b7) images up to the fifth scale of decomposition. The texture descriptors are then acquired from each of the subimages up to the five scales of decomposition. Further, two variants of support vector machine (SVM), linear SVM and radial basis function kernel SVM, were employed as classifiers. The classification accuracy of the proposed and existing texture descriptors was compared. The BWT-based uniform completed local binary pattern (BWTCLBPu2) texture descriptor achieved the best classification accuracy of 95.07 ± 0.72% at the third scale of decomposition. The classification accuracy is produced by linear SVM classifier for full feature (1416) vector data. In order to overcome the effect of curse of dimensionality, the minimal-redundancy–maximal-relevance feature selection method is employed to select the best subset of feature vector data. This approach has resulted in improved classification accuracy of 96.60 ± 0.80% (450) by linear SVM classifier.

A Bit Plane based Filtering Technique for Drusen in Age-related Macular Degeneration

Objective: The authors propose to develop a new image filter technique based on bit planes of retinal fundus image to enhance the image preprocessing task. Method/Statistical Analysis: The most two significant bit planes of an image are taken and preprocessed using an image matrix filter. The image arithmetic operations are used to enhance the new filter. The sample graded portions of each of the 60 AMD images are processed to study this filter technique. The effect of the technique is studied with basic statistics tests using mean, median, standard deviation and t-test, testing on column vectors of images. Findings: The basic statistic measures such as mean, median, range and standard deviations are applied on image pixel data before and after application of filter technique. The standard deviation of post filtered graded portions of the image shows a higher value than that of the graded portions of unfiltered image, indicating that there is a positive effect of filtering. The effectiveness of the technique is also studied with a statistical paired t-test. This test is used to find the effect of processing bit planes of the respective graded pixel portions in the image samples. This statistical evidence of the result reveal that the null hypothesis is rejected to conclude that image is enhanced for further processing of drusen spots in AMD. Application/Improvements: The least bit planes of the graded portions of the image may be furthur processed to enable easier classification of different types of drusens in the image.

A fingerprint matching algorithm using bit-plane extraction method with phase-only correlation

This paper introduces a new method in fingerprint feature extraction based on bit-plane. A bit-plane image requires smaller storage space than a greyscale image. Region of interest (ROI) of a fingerprint image is extracted by using a modified blob analysis method, and then the core point of the ROI is detected for dimension reduction process. Before bit-plane is extracted, the fingerprint image is enhanced by using Fourier transform. Bit-plane 7 of the enhanced image is used as the input for fingerprint matching with phase-only correlation (POC) function. Experiment results showed that the storage space requirement for a fingerprint database could be reduced up to 87% per image for a bit-plane image compared with a greyscale image. The proposed fingerprint matching algorithm achieves 81.16% of recognition rate on FVC2002-Db1a database and 89.78% on FingerDOS database.

Quantum image coding with a reference-frame-independent scheme

For binary images, or bit planes of non-binary images, we investigate the possibility of a quantum coding decodable by a receiver in the absence of reference frames shared with the emitter. Direct image coding with one qubit per pixel and non-aligned frames leads to decoding errors equivalent to a quantum bit-flip noise increasing with the misalignment. We show the feasibility of frame-invariant coding by using for each pixel a qubit pair prepared in one of two controlled entangled states. With just one common axis shared between the emitter and receiver, exact decoding for each pixel can be obtained by means of two two-outcome projective measurements operating separately on each qubit of the pair. With strictly no alignment information between the emitter and receiver, exact decoding can be obtained by means of a two-outcome projective measurement operating jointly on the qubit pair. In addition, the frame-invariant coding is shown much more resistant to quantum bit-flip noise compared to the direct non-invariant coding. For a cost per pixel of two (entangled) qubits instead of one, complete frame-invariant image coding and enhanced noise resistance are thus obtained.

Bitplane Image Coding With Parallel Coefficient Processing.

Image coding systems have been traditionally tailored for multiple instruction, multiple data (MIMD) computing. In general, they partition the (transformed) image in codeblocks that can be coded in the cores of MIMD-based processors. Each core executes a sequential flow of instructions to process the coefficients in the codeblock, independently and asynchronously from the others cores. Bitplane coding is a common strategy to code such data. Most of its mechanisms require sequential processing of the coefficients. The last years have seen the upraising of processing accelerators with enhanced computational performance and power efficiency whose architecture is mainly based on the single instruction, multiple data (SIMD) principle. SIMD computing refers to the execution of the same instruction to multiple data in a lockstep synchronous way. Unfortunately, current bitplane coding strategies cannot fully profit from such processors due to inherently sequential coding task. This paper presents bitplane image coding with parallel coefficient (BPC-PaCo) processing, a coding method that can process many coefficients within a codeblock in parallel and synchronously. To this end, the scanning order, the context formation, the probability model, and the arithmetic coder of the coding engine have been re-formulated. The experimental results suggest that the penalization in coding performance of BPC-PaCo with respect to the traditional strategies is almost negligible.

Hiding in encrypted images: a three tier security data hiding technique

This paper presents a new crypto domain data hiding technique based on Intermediate Significant Bit Plane Embedding (ISBPE). The cover image is encrypted; the information to be secured is scrambled, and then embedded in the Intermediate Significant Bit (ISB) planes of encrypted cover image, at the locations determined by a Pseudorandom Address Vector (PAV). The pseudorandom embedding of the scrambled data in the ISB planes of encrypted image results in a three tier security of the data to be secured. The ISBPE embedding results in an important advantage that the proposed scheme becomes completely robust to commonly employed attack of Least Significant Bit (LSB) removal/replacement. A novel concept of embedding a very small size fragile watermark in addition to the secret information has been used which facilitates early tamper detection. This feature could save crucial processor time in critical situations of national security issues/warfare etc. Experimental results show that the proposed scheme is more robust to various signal processing attacks like Joint Picture Expert Group compression, Additive White Gaussian Noise and `salt and pepper' noise as compared to conventional LSB based embedding techniques. Comparison results with some well-known techniques show that besides providing high degree of security and robustness to various malicious attacks the proposed technique is capable of embedding a fairly large amount of secret data in the host image while maintaining a good stego-image quality.

Face Fusion Recognition Based on Bit-Plane Image

Feature extraction is the main technology in the process of human face recognition system which is widely uti- lized as the mainstream approach in identity verification system. However, the traditional human face feature extraction algorithm of Eigenface has strict restraint on the light in the original human face image, posing as a hindrance in the prac- tical application. Aiming at this problem, this paper proposes a novel method for human face feature extraction based on human face bit-plane image using Eigenface and Fisher's linear discriminant analysis at the same time. The constitution and characteristic of the fusion recognition method is analyzed and the experiments on performance study are proved the superiority of the new fusion recognition method.

Design of image cipher using latin squares

In this paper, we introduce a symmetric-key Latin square image cipher (LSIC) for grayscale and color image encryption. Our main contributions include (1) we propose new Latin square image encryption primitives including Latin Square Whitening, Latin Square S-box and Latin Square P-box; (2) we develop probabilistic image encryption by embedding random noise into the least significant bit-plane of images; and (3) we design a new loom-like 2D substitution–permutation network maintaining good confusion and diffusion properties with extra error tolerance. Theoretical analysis and simulation results show that the proposed method has many desired properties of a secure cipher, shows robustness against different attack models, and outperforms state of the art suggested by many peer algorithms. Open-source implementation can be found on the webpage https://sites.google.com/site/tuftsyuewu/source-code.

SVM correction based geometrically invariant digital watermarking algorithm

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric distortions. Based on the Support Vector Machine (SVM) geometric distortions correction, we propose a new image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the significant bitplane image is extracted from original host, and DWT is performed on the significant bitplane image. Then, the corresponding low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by adaptively modulating the selected wavelet coefficients in small blocks. The main steps of digital watermark detecting procedure include: (1) the significant bitplane image is extracted from test image, and some low-order pseudo-Zernike moments of the significant bitplane image are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a SVM training model can be obtained; (3) the test image is corrected with the well trained SVM model; (4) the digital watermark is extracted from the corrected test image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.

A Wavelet Based Scheme for Video Watermarking

In rapid growth of network distributions of images and video, there is an urgent need for copyright protection against pirating. Different digital image and video watermarking schemes have been proposed to address this issue of ownership identification. This paper proposes a novel scheme for video watermarking scheme using discrete wavelet transform to protect the copyright of digital images. The efficiency of the proposed video watermarking technique is attained by two major steps: 1) Watermark embedding process and 2) Watermark extraction process. Before embedding the watermark into the digital video, the input video sequence is segmented into shots using well known shot segmentation technique. The segmented video shots are partitioned into number of frames for the embedding process. The proposed method renders the utilization of the grayscale image as a watermark to embed into the digital video sequence. The grayscale image is sliced into bit planes for analyzing the each bit of the image. Subsequently, the sliced bit plane images are permuted and each permuted watermark images are embedded into each frame of the segmented shots with the aid of the watermark embedding process. Then the recovery of the watermark is achieved with the help of the watermark extraction process. The experimentation results shows that the proposed video watermarking scheme provide better results with higher accuracy. The proposed scheme is robust against the various attacks such as frame dropping, frame averaging attack.

Robust color image retrieval using visual interest point feature of significant bit-planes

In this paper, we propose a robust color image retrieval method using visual interest point feature of significant bit-planes. We firstly extract the visually significant bit-plane image from the original color image according to the bit-plane theory and noise attack characteristic. And then, we extract the visual interest points from the original color image by using the significant bit-plane image and multi-scale Harris–Laplace detector, and construct the fuzzy color histogram of visual interest points. We finally compute the similarity between color images by using the fuzzy color histogram of visual interest points. Experiments on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches. Especially, it can retrieve the noise-attacked (including blurring, sharpening, and illumination, etc.) image effectively. ► To extract the visual interest points from the original color image by using the significant bit-plane image and multi-scale Harris–Laplace detector. ► To construct the fuzzy color histogram of visual interest points. ► To compute the similarity between color images by using the fuzzy color histogram of visual interest points.

An efficient bit plane X-OR algorithm for irreversible image steganography

The science of hiding secret information in a message is known as steganography; hence, the presence of secret information is concealed. It is the method of hiding cognitive content in same or another media to avoid detection by intruders. This paper introduces a new method wherein irreversible steganography is used to hide an image in the same medium so that the secret data is masked. The secret image is known as a payload and the carrier is known as a cover image. X-OR operation is used amongst mid level bit planes of carrier image and high level bit planes of secret image to generate new low level bit planes of the stego image. Recovery process includes the X-ORing of low level bit planes and mid level bit planes of the stego image. Based on the result of the recovery, subsequent secret image is generated. An RGB colour image is used as carrier and the secret image is a grayscale image of dimensions less than or equal to the dimensions of the carrier image. The proposed method greatly increases the embedding capacity without significantly decreasing the PSNR value.

Hierarchical stereo matching with image bit-plane slicing

We propose a new stereo matching framework based on image bit-plane slicing. A pair of image sequences with various intensity quantization levels constructed by taking different bit-rate of the images is used for hierarchical stereo matching. The basic idea is to use the low bit-rate image pairs to compute rough disparity maps. The hierarchical matching strategy is then carried out iteratively to update the low confident disparities with the information provided by extra image bit-planes. It is shown that, depending on the stereo matching algorithms, even the image pairs with low intensity quantization are able to produce fairly good disparity results. Consequently, variate bit-rate matching is performed only regionally in the images for each iteration, and the average image bit-rate for disparity computation is reduced. Our method provides a hierarchical matching framework and can be combined with the existing stereo matching algorithms. Experiments on Middlebury datasets show that the proposed technique gives good results compared to the conventional full bit-rate matching.

A robust digital watermarking algorithm in undecimated discrete wavelet transform domain

In this paper, a robust digital image watermarking algorithm is proposed based on fuzzy support vector machine (FSVM) correction. The significant bitplane image is extracted from original host, and undecimated discrete wavelet transform (UDWT) is performed on the significant bitplane image. The lowpass subband is divided into small blocks, and digital watermark is embedded into original host by adaptively modulating the selected wavelet coefficients in small blocks. In order to resist geometric distortion attacks, FSVM is utilized to learn geometric distortion parameters by using low order Zernike moments. The watermark extraction is carried out after watermarked image has been synchronized without original host. Simulation also shows outstanding robustness of the proposed algorithm against most common image processing operations and geometric distortions.

Properties of Images in LSB Plane

Each pixel of an Image (assuming 24 bit BMP) is stored in the three 8 bits corresponding to three colors. Bit Plane Slicing is the technique of breaking image into 8 planes corresponding to 8 different bit positions. The bit plane image corresponding to the plane of the most significant bit (MSB) has the maximum contribution to the total image and forms the majority of the visually significant image data and planes corresponding to other lower bit positions contribute only the subtle details of the image. And the bit-plane image corresponding to the LSB of the RGB value has the minimum contribution of only 1 out of total of 255 Intensity level to the total image. In spite of this the LSB Plane of the image is not insignificant. In fact it is used in Image Steganography for hiding secret information in the images. Image based Steganography is a potent tool used by Terrorists and Criminal organizations for securely broadcasting, dead-dropping and communicating malicious secret messages over the internet by hiding them in the images. Our cyber space is full of such mala-fide images containing hidden secret information. The most difficult aspect of tracking such malicious images is the lack of efficient and fast algorithms for identifying and isolating them from the bulk of innocent images. Analysis of the pixels in the LSB Plane of the image is very effective technique of identification of Distributing Spatial Domain Steganographic Algorithms. In this paper the properties of Information Pixels pixel present in the LSB Plane of any stego image generated by Distributing Algorithms is analyzed in detail. These properties will be very useful in identification of malafide stego images lurking in our cyber space. All the images and tables used in this paper are generated using MATLAB ©Image Processing Tool Box.

Scanning Order Strategies for Bitplane Image Coding

Scanning orders of bitplane image coding engines are commonly envisaged from theoretical or experimental insights and assessed in practice in terms of coding performance. This paper evaluates classic scanning strategies of modern bitplane image codecs using several theoretical-practical mechanisms conceived from rate-distortion theory. The use of these mechanisms allows distinguishing those features of the bitplane coder that are essential from those that are not. This discernment can aid the design of new bitplane coding engines with some special purposes and/or requirements. To emphasize this point, a low-complexity scanning strategy is proposed. Experimental evidence illustrates, assesses, and validates the proposed mechanisms and scanning orders.

Improved Adaptive Block Truncation Coding for Image Compression

ABSTRACT The Block Truncation Coding (BTC) is one of the lossy image compression algorithms. In this paper, we have proposed a method called the Improved Adaptive Block Truncation Coding (IABTC) based on Adaptive Block Truncation Coding (ABTC). The feature of inter-pixel redundancy is exploited to reduce the bit-rate further by retaining the quality of the reconstructed images. The proposed method outperforms the existing BTC based methods both in terms of bit-rate and PSNR values. General Terms Image Compression Keywords Compression, block, bit plane, bpp, PSNR, quantizers 1. INTRODUCTION Data compression algorithms lead to reduction in transmission time and storage cost. Image compression techniques are classified into lossy and lossless techniques. In lossless techniques, the reconstructed image will be the exact replica of the original image whereas in lossy techniques, the reconstructed image will be an approximation of the original image. One of the main goals for image data compression is to reduce redundancy in the image block as much as possible. That is, it is very important to represent an image with as few bits as possible while maintaining good image quality. xiBoth compression and decompression algorithms should be simple and efficient. BTC is one of the simple and easy to implement image compression algorithms [1]. It comes under lossy image compression techniques. Though some data is lost, it does not make much difference to the Human Visual System (HVS). is the original pixel value of the block. One such lossy image compression techniques is Block Truncation Coding (BTC) technique. BTC has the advantage of being easy to implement when or 0s for each block is called acompared to Vector Quantization [8] and Transform Coding [9, statistical moments 10]. BTC achieves 2 bits per pixel (bpp) with low computational complexity [11]. Many techniques with modifications to BTC have been proposed to reduce the bit-rate obtained with normal BTC: Gallagher [12] proposed a Median Filter Roots method; Yung-Gi Wu [13] proposed Probability based Block Truncation Image Bitplane Coding; Yu-Chen Hu [14] presented a Modified BTC with Predictive Technique and Bitplane Coding with Edge Pattern. In this paper, we have presented a method named Improved Adaptive Block Truncation Coding (IABTC) based on the Adaptive Block Truncation Coding (ABTC) method. Few other existing Block Truncation Coding (BTC) based methods are discussed in Section 2. The proposed method is explained in Section 3. Results are discussed in Section 4 and the conclusion is given in Section 5.