Adjacent Pixel Values Research Articles

Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.

A Probabilistic Chaotic Image Encryption Scheme

This paper proposes a probabilistic image encryption scheme that improves on existing deterministic schemes by using a chaining mode of chaotic maps in a permutation-masking process. Despite its simplicity, the permutation phase destroys any correlation between adjacent pixel values in a meaningful image. The masking phase, however, modifies the pixel values of the image at hand using pseudorandom numbers with some other initiated random numbers so that any slight change in the plain image spreads throughout the corresponding cipher image. These random numbers ensure the generation of distinct cipher images for the same plain image encryption, even if it is encrypted multiple times with the same key, thereby adding some security features. Simulations show that the proposed scheme is robust to common statistical and security threats. Furthermore, the scheme is shown to be competitive with existing image encryption schemes.

An image encryption algorithm based on spatiotemporal chaos and middle order traversal of a binary tree

This paper proposes an image encryption algorithm based on spatiotemporal chaos and middle order traversal of a binary tree. Firstly, other programming software is used to perform the middle order traversal, and the plaintext image is sorted according to the middle order traversal sequence on the permutation. Secondly, the chaotic sequence is generated using the coupled map lattice to set the chaotic interference value. Finally, the XOR operation between the adjacent pixel values of the replacement image is completed to generate the ciphertext matrix. The simulation and experimental results show that the proposed algorithm can resist typical attacks and has good robustness.

Research on reversible image steganography of encrypted image based on image interpolation and difference histogram shift

In view of the mutual restriction between the image encryption and information hiding process in the ciphertext domain reversible algorithm, the existing ciphertext domain image reversible image steganography algorithm can hardly take into account the performance of security and hiding capacity. This paper proposes a reversible image steganography algorithm for encrypted images based on a new interpolation image and difference histogram shift. The pixel position of the carrier image is changed through the double scrambling operation of random image block recombination and Arnold transform. The chaotic sequence generated by logistic mapping is used as the diffusion sequence of the encryption algorithm, and DNA encoding and calculation are performed respectively to obtain the final encrypted image. According to the newly proposed interpolation algorithm, the encrypted image is enlarged, determine the pixel value of the target interpolated image according to the pixel value of the encrypted image, calculate the difference between the three adjacent pixel values of the target interpolated image, and use the difference histogram translation algorithm to convert the secret Information is embedded in an interpolated image, and only one bit of the pixel value can be modified in a single embedding process, and the embedding method is simple and effective. Experimental results show that under the premise of ensuring information security and image quality, the embedding capacity has also been greatly improved, reaching 1,572,864 bits. When the embedding rate is 0.67 bpp, the PSNR can also reach 58 dB and the image quality is maintained better.

A novel hybrid image encryption–compression scheme by combining chaos theory and number theory

Compression and encryption are often performed together for image sharing and/or storage. The order in which the two operations are carried out affects the overall efficiency of digital image services. For example, the encrypted data has less or no compressibility. On the other hand, it is challenging to ensure reasonable security without downgrading the compression performance. Therefore, incorporating one requirement into another is an interesting approach. In this study, we propose a novel hybrid image encryption and compression scheme that allows compression in the encryption domain. The encryption is based on Chaos theory and is carried out in two steps, i.e., permutation and substitution. The lossless compression is performed on the shuffled image and then the compressed bitstream is grouped into 8-bit elements for substitution stage. The lossless nature of the proposed method makes it suitable for medical image compression and encryption applications. The experimental results shows that the proposed method achieves the necessary level of security and preserves the compression efficiency of a lossless algorithm. In addition, to improve the performance of the entropy encoder of the compression algorithm, we propose a data-to-symbol mapping method based on number theory to represent adjacent pixel values as a block. With such representation, the compression saving is improved on average from 5.76% to 15.45% for UCID dataset.

Single-image super-resolution based on local biquadratic spline with edge constraints and adaptive optimization in transform domain

This paper proposes a novel single-image super-resolution method based on local biquadratic spline with edge constraints and adaptive optimization in transform domain. The complex internal structure of the image makes the values of adjacent pixels often differ greatly. Using surface patches to interpolate image blocks can avoid large surface oscillation. Because the quadratic spline has better shape-preserving property, we construct the biquadratic spline surface on each image block to make the interpolation more flexible. The boundary conditions have great influence on the shape of local biquadratic spline surfaces and are the keys to constructing surfaces. Using edge information as a constraint to calculate them can reduce jagged and mosaic effects. To decrease the errors caused by surface fitting, we propose a new adaptive optimization model in transform domain. Compared with the traditional iterative back-projection, this model further improves the magnification accuracy by introducing SVD-based adaptive optimization. In the optimization, we convert similar block matrices to the transform domain by SVD. Then the contraction coefficients are calculated according to the non-local self-similarity, and the singular values are contracted. Experimental comparison with the other state-of-the-art methods shows that the proposed method has better performance in both visual effect and quantitative measurement.

High Bit-Depth Image Acquisition Framework Using Embedded Quantization Bias

In this paper, we present a novel image acquisition framework capable of reconstructing high bit-depth images using an array of low bit-depth scalar quantizers. Our key contribution is a codesign of pixel quantization and reconstruction in image acquisition pipeline. Different from the traditional image acquisition scheme, where each pixel value is quantized and reconstructed independently, the proposed framework is designed based on the interpixel correlations in local image regions. The interpixel correlations imply that quantized pixel values of adjacent locations can be interpreted as multiple descriptions of a common pixel value. Because combining multiple descriptions leads to reduced uncertainty, a pixel value with higher bit depth can be reconstructed by exploiting the interpixel correlations. In this paper, we propose to inject an embedded quantization bias (EQB) to the prequantized image signal and feed the sum to scalar quantizers. Injecting EQB has the same effect as shifting the quantizers by different amounts at different pixel locations, driving the quantized values of adjacent pixels to form informative descriptions of a common pixel value. We present comprehensive studies on this framework, including the optimal design of the EQB signal, the reconstruction strategies for noisy input, and generalized assumptions on interpixel correlations. We show in the experiments that the proposed image acquisition framework significantly outperforms the competing methods and effectively improves the quality of the reconstructed images.

IMPLEMENTASI METODE PIXEL VALUE DIFFERENCING UNTUK PENYEMBUNYIAN PESAN PADA CITRA DIGITAL

The development of information technology is very fast, causing a lot of security holes that can be misused by people who are not responsible so that it can harm certain parties. Digital images are chosen as a container for inserting messages because digital images have sufficient size to hold the message and digital images are often used in information exchange so as not to invite suspicion from irresponsible parties. The information to be sent is hidden in a digital image, then the digital image is sent as normal data, so that third parties do not suspect that there is confidential information inside. Information that is hidden in the digital image can be extracted again by the recipient of the message. Pixel Value Differencing (PVD) works on a pair of adjacent pixel values (adjacent pixels). The advantage of the Pixel Value Differencing (PVD) method is that the capacity of the image generated to insert a message can be smaller than its original size, the processing time of this method is quite fast, after the message is inserted, the image quality has good quality. But this method also has disadvantages, because it is not resistant to manipulation.Keywords: Steganography, Digital Image, Pixel Value Differencing

Chinese Remainder Theorem-Based Secret Image Sharing with Small-Sized Shadow Images

Secret image sharing (SIS) with small-sized shadow images has many benefits, such as saving storage space, improving transmission time, and achieving information hiding. When adjacent pixel values in an image are similar to each other, the secret image will be leaked when all random factors of an SIS scheme are utilized for achieving small sizes of shadow images. Most of the studies in this area suffer from an inevitable problem: auxiliary encryption is crucial in ensuring the security of those schemes. In this paper, an SIS scheme with small-sized shadow images based on the Chinese remainder theorem (CRT) is proposed. The size of shadow images can be reduced to nearly 1 / k of the original secret image. By adding random bits to binary representations of the random factors in the CRT, auxiliary encryption is not necessary for this scheme. Additionally, reasonable modifications of the random factors make it possible to incorporate all advantages of the CRT as well, including a ( k , n ) threshold, lossless recovery, and low computation complexity. Analyses and experiments are provided to demonstrate the effectiveness of the proposed scheme.

An effective reversible data hiding algorithm using Huffman coding and pixel value prediction algorithm based on the similarity between adjacent pixel values

In this paper, an effective reversible data hiding algorithm was proposed to increase the amount of data to be embedded data into cover image. Generally, there is similarity between adjacent pixel values in natural images. In the proposed algorithm, a technique that accurately predicts pixel value using adjacent pixel values was used and Huffman coding algorithm was used to increase the amount of data to be embedded into cover image. The proposed reversible data hiding algorithm increases greatly the amount of data embedded into cover image. Also, the visual quality of the secrete data embedded image is very good. So, it is difficult to know whether secrete data has been embedded into the image. Also, using the proposed algorithm, secrete data can be embedded at various levels. The experimental results show that the proposed reversible data hiding algorithm is superior to APD algorithm. Therefore, the proposed algorithm is very useful for reversible data hiding applications.

3D sunken relief generation from a single image by feature line enhancement

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient.

Video data image retrieval using – BRICH

PurposeData mining is the process of detecting knowledge from a given huge data set. Among the data set, multimedia is the data which contains diverse data such as audio, video, image, text and motion. In this growing field of video data, mining the video data plays vital role in the field of video data mining. In video data mining, video data are grouped into frames. In this vast amount of video frames, the fast retrieval of needed information is important one. This paper aims to propose a Birch-based clustering method for content-based image retrieval.Design/methodology/approachIn image retrieval system, image segmentation plays a very important role. A text file, normally, is divided into sections, that is, piece, sentences, word and character for this information which are organized and indexed effectively like in a video, the information is dynamic in nature and this information is converted to static for easy retrieval. For this, video files are divided into a number of frames or segments. After the segmentation process, images are trained for retrieval process, and from these, unwanted images are removed from the data set. The noise or unwanted image removal pseudo-code is shown below. In the code image, pixel value represents the value of the difference between the two adjacent image pixel values. By assuming a threshold for the image value, the duplicate images are found. After finding the duplicate image, it is removed from the data set. Clustering is used in many applications as a stand-alone tool to get insight into data distribution and as a pre-processing step for other algorithms (Ester et al., 1996). Specifically, it is used in pattern recognition, spatial data analysis, image processing, economic science document classification, etc. Hierarchical clustering algorithms are classified as agglomerative or divisive. BRICH uses clustering attribute (CA) and clustering feature hierarchy (CA_Hierarchy) for the formation of clusters. It perform multidimensional data objects. Every BRICH algorithm based on the memory-oriented information, that is, memory constrains, is involved in the processing of the data sets. This information is represented in Figures 6-10. For forming clusters, they use the amount of object in the cluster (A), the sum of all points in the data set (S) and need the square value of the all objects (P).FindingsThe proposed technique brings an effective result for cluster formation.Originality/valueBRICH uses a novel approach to model the degree of inter-connectivity and closeness between each pair of clusters that takes into account the internal characteristics of the clusters themselves.

BNB Method for No-Reference Image Quality Assessment

It is challenging to quantitatively assess image quality in real time without a reference image while achieving human-level perception performance. In this paper, we present a no-reference (NR) image quality assessment (IQA) method called BNB (an acronym for blurriness, noisiness, and blockiness). Our BNB method quantifies the blurriness, noisiness, and blockiness of a given image, which are considered as three critical factors affecting users’ quality of experience. This method is rooted in the observation that for any image, the difference between any two adjacent pixel values follows a generalized Laplace distribution with zero mean. This Laplace distribution changes differently when the image experiences various types of artifacts, i.e., blurriness, noisiness, and blockiness. To construct a metric for each BNB artifact, we first extract features for each type of artifacting from the changing Laplace distribution and then identify the quantitative relationship between the feature value and the variation of the artifact. Given human perception scores of a popular image database, we use the k-nearest neighbor algorithm to map our three BNB metrics of an image to a human perception score. Experimental results reveal that the image quality score obtained from our BNB method has higher correlation with human perceptual scores in addition to requiring notably less computation compared with existing NR IQA methods.

Restoration for Noise Removal in Quantum Images

Quantum computation has become increasingly attractive in the past few decades due to its extraordinary performance. As a result, some studies focusing on image representation and processing via quantum mechanics have been done. However, few of them have considered the quantum operations for images restoration. To address this problem, three noise removal algorithms are proposed in this paper based on the novel enhanced quantum representation model, oriented to two kinds of noise pollution (Salt-and-Pepper noise and Gaussian noise). For the first algorithm Q-Mean, it is designed to remove the Salt-and-Pepper noise. The noise points are extracted through comparisons with the adjacent pixel values, after which the restoration operation is finished by mean filtering. As for the second method Q-Gauss, a special mask is applied to weaken the Gaussian noise pollution. The third algorithm Q-Adapt is effective for the source image containing unknown noise. The type of noise can be judged through the quantum statistic operations for the color value of the whole image, and then different noise removal algorithms are used to conduct image restoration respectively. Performance analysis reveals that our methods can offer high restoration quality and achieve significant speedup through inherent parallelism of quantum computation.

개선된 곡면 특성 추정 기법을 이용하는 영상 확대 기법

자연 영상에는 일반적으로 지역성이 존재하기 때문에 인접한 픽셀 값들은 매우 유사한 값을 갖는다. 유사한 픽셀 값을 갖는 인접 픽셀들을 사용하여 실제 영상의 곡면 특성을 추정할 수 있다. 본 논문에서는 인접 픽셀 값들을 사용하여 실제 영상에 존재하는 곡면 특성을 정밀하게 추정하고, 곡면 특성을 반영하여 보간 값을 구하는 효과적인 기법을 제안하였다. 제안된 기법을 사용하여 보간 값을 구하고자 하는 부분의 곡면 특성을 추정한 후, 추정된 곡면 특성을 충실하게 반영하는 보간 값을 구하여 확대 영상을 생성하는 효과적인 영상 확대 기법은 확대 영상의 화질을 향상시킨다. 실험을 통하여 제안된 기법의 성능이 기존의 기법에 비하여 우수함을 확인하였다. In natural images, there is generally locality, and the values of adjacent pixels are similar. It is possible to estimate the curved surface characteristics of the original image using adjacent pixels having similar pixel values. In this paper, after precisely estimating the characteristics of the curved surface existing in the image, interpolation values are obtained so as to faithfully reflect the estimated characteristics of the curved surface, We propose an effective image enlarging method that generates an enlarged image using the obtained interpolation values. The image enlarged by the proposed method maintains the curved surface characteristics of the original image, and thus the image quality of the enlarged image is improved. Experimental results show that the image quality of the proposed method is superior to that of the conventional techniques.

영상의 지역성과 인접 픽셀 차분 시퀀스를 이용하는 가역 데이터 임베딩 기법

본 논문에서는 영상의 지역성과 인접 픽셀 차분시퀀스를 이용하는 가역 데이터 임베딩 기법을 제안하였다. 자연영상에는 일반적으로 지역성이 존재한다. 영상의 지역성을 이용하여 인접한 픽셀 값을 예측하는 기법을 기존의 기법인 APD(Adjacent Pixel Difference) 기법에 적용하여 임베딩 가능한 데이터 량을 증가 시키고 다양한 레벨로 데이터 임베딩을 가능하게 하는 가역 데이터 임베딩 기법을 제안하였다. 실험결과를 통하여 제안된 기법의 우수성을 확인하였다. In this paper, reversible data embedding scheme was proposed using the locality of image and the adjacent pixel difference sequence. Generally, locality exists in natural image. The proposed scheme increases the amount of embedding data and enables data embedding at various levels by applying a technique of predicting adjacent pixel values using image locality to an existing technique APD(Adjacent Pixel Difference). The experimental results show that the proposed scheme is very useful for reversible data embedding.

Computer-aided grading of gliomas based on local and global MRI features

Background and objectivesA computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. MethodsThe acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. ResultsPerformances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement κ = 0.698, p < 0.001. ConclusionsNumerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro-spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re-labeling and contrast enhancement using non-linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1-weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.

Coding Algorithm Based on the Simplified Semilogarithmic Compression Law for Discrete Input Samples with Laplacian Distribution

In this paper, a novel simple algorithm based on the simplified semilogarithmic quantizer is proposed for Laplacian source coding. An analysis of the signal to quantization noise ratio (SQNR) of the simplified semilogarithmic quantizer is provided in the wide variance range for the cases of discrete input samples and continual input samples with Laplacian distribution. For the assumed discrete input samples and continual input samples it is shown that the higher SQNR is achieved by the proposed semilogarithmic quantizer when compared to the uniform quantizer. Another major contribution of this paper is the proof that in the area of smaller variance values the simplified semilogarithmic quantizer provides significantly higher SQNR values than the uniform quantizer, which is of great importance, especially in image coding, where the range of smaller variances is more probable than the range of higher variance values. Due to the simple realization structure and higher achieved SQNR than the uniform quantizer, one can expect that the proposed algorithm will be very applicable in coding of signals which, as well as speech signals and signals of difference between adjacent pixel values of image, follow Laplacian distribution. DOI: http://dx.doi.org/10.5755/j01.eee.20.3.6679

인접 픽셀 값 정보를 이용하는 효율적인 확대 영상의 화질 개선 기법

본 논문에서는 확대 영상의 화질을 개선하기 위하여 인접픽셀 값 정보를 이용하여 영상의 단순 볼록 곡면과 단순 오목 곡면을 추정하는 기법과 영상이 단순 볼록 곡면과 단순 오목 곡면의 특성을 반영하여 확대 영상을 구성하는 기법을 제안하였다. 제안된 기법을 사용하여 확대 영상을 구성하면 기존의 기법들을 사용하여 확대한 영상보다 실제 영상에 더욱 충실하게 된다. 제안된 기법을 적용하여 확대한 영상의 화질향상 정도를 측정하기 위하여 PSNR(Peak Signal to Noise Ratio)을 사용하였다. 제안된 기법을 적용하여 확대한 영상들의 PSNR 값들은 기존의 기법들을 적용하여 확대한 영상들의 PSNR 값보다 큰 것을 확인하였다. To improve the quality of magnified image, two schemes were proposed. The one is used to estimate simple convex surface and simple concave surface using the information of adjacent pixel values, and the other scheme is used to produce magnified image using the characteristics of simple convex surface and simple concave surface. The magnified image using the proposed scheme is more similar to real image than the magnified image using the previous schemes. The PSNR values of the magnified images using the proposed scheme are greater than those of the magnified images using the previous interpolation schemes.