Original Pixel Research Articles

Automated Hematoma Detection and Outcome Prediction in Patients With Traumatic Brain Injury.

To develop a tool for automated subtype classification and segmentation of intracranial hemorrhages (ICH) on CT scans of patients with traumatic brain injury (TBI). Furthermore, outcome prediction for patients can effectively facilitate patient management. This study presents a cascade framework for two-stage segmentation and multi-label classification. The hematoma region of interest (ROI) is localized, and then the ROI is cropped and resized to the original pixel size before being input into the model again to obtain the segmentation results. In multilabel classification, the mask obtained from automatic segmentation is superimposed onto the corresponding ROI and CT slices, respectively, to constitute the input image. Subsequently, the ROI image is employed as the local network input to obtain local features. Third, the CT image is utilized to construct a feature extraction network to obtain global features. Ultimately, the local and global features are fused dimensions in the pooling layer,and calculated to generate the final retrieval results. For the prediction of 14-day in-hospital mortality, automatically extracted hematoma subtype and volume features were integrated to enhance the widely used CRASH model. The proposed segmentation method achieves the best estimates on the Dice similarity coefficient and Jaccard Similarity Index. The proposed multilabel classification method achieved an average accuracy of 95.91%. For mortality prediction, the best model achieved an average area under the receiver operating characteristic curve (AUC) of 0.91 by 5-fold cross-validation. The proposed method enhances the precision of hematoma segmentation and subtype classification. In clinical settings, the method can streamline the evaluation of ICH for radiologists, and the automatically extracted features are anticipated to facilitate prognosis assessment.

SFDiff: Diffusion model with sufficient spatial‐Fourier frequency information interaction for low‐light image enhancement

AbstractDiffusion models are increasingly applied in low‐light image enhancement tasks due to their exceptional capability to model data distributions, but most current methods focus only on the original pixel space and neglect the potential of Fourier frequency information. In this article, SFDiff is proposed, a novel low‐light image enhancement method that integrates Fourier frequency information into the diffusion process. Specifically, Fourier transforms are applied at both the image and feature levels to separately enhance the amplitude and phase components, which restores global illumination degradation and positional information. Then a Spatial‐Frequency Fusion (SFF) block is used to fully integrate and interact with the information across spatial and frequency domains. Since illumination degradation is primarily manifested in the amplitude component, a loss function based on maximum likelihood learning is employed to constrain the amplitude component at each step of the sampling process, ensuring that the reverse process maintains an optimal trajectory. Owing to the streamlined network design and the fact that the Fourier transform requires no extra parameters, SFDiff achieves a reduction in parameters of over compared to several state‐of‐the‐art (SOTA) diffusion models and delivers high‐quality enhancement results on multiple real‐world datasets. The code is available at https://github.com/MrWan001/SFDiff.

The impact of resolution and flipping on image classification

The initial stage of image classification involves image preprocessing, which includes considerations such as the object's position within the image, the background, ambient lighting, and the viewing angle. These factors can introduce variations in the original pixel data, and consequently, applying diverse preprocessing methods can yield varying levels of accuracy in the collected data. This study explores the relationship between preprocessing techniques and the performance of deep learning models. In this paper, images are processed by both reducing and increasing their resolution and by applying 180° horizontal and vertical flips. Subsequently, the Edge Impulse platform (https://edgeimpulse.com/) is employed for image classification. The objective of this paper is to assess the impact of resolution adjustments and flipping on image classification accuracy through a series of repeated experimental analyses.

Frequency-Oriented Transformer for Remote Sensing Image Dehazing.

Remote sensing images are inevitably affected by the degradation of haze with complex appearance and non-uniform distribution, which remarkably affects the effectiveness of downstream remote sensing visual tasks. However, most current methods principally operate in the original pixel space of the image, which hinders the exploration of the frequency characteristics of remote sensing images, resulting in these models failing to fully exploit their representation ability to produce high-quality images. This paper proposes a frequency-oriented remote sensing dehazing Transformer named FOTformer, to explore information in the frequency domain to eliminate disturbances caused by haze in remote sensing images. It contains three components. Specifically, we developed a frequency-prompt attention evaluator to estimate the self-correlation of features in the frequency domain rather than the spatial domain, improving the image restoration performance. We propose a content reconstruction feed-forward network that captures information between different scales in features and integrates and processes global frequency domain information and local multi-scale spatial information in Fourier space to reconstruct the global content under the guidance of the amplitude spectrum. We designed a spatial-frequency aggregation block to exchange and fuse features from the frequency domain and spatial domain of the encoder and decoder to facilitate the propagation of features from the encoder stream to the decoder and alleviate the problem of information loss in the network. The experimental results show that the FOTformer achieved a more competitive performance against other remote sensing dehazing methods on commonly used benchmark datasets.

Confusion and Diffusion Techniques for Image Encryption Process Based on Chaos System

Face recognition uses biometric technologies to identify humans based on their facial characteristics. This method is commonly used to restrict information access control. The benefits of face recognition systems encompass their ease of use and security. The human face recognition process consists of face detection, face tracking, and face recognition. The process uses some algorithms: the Viola-Jones for face detection, the Kanade-Lucas-Tomasi (KLT) for face tracking, and the principal component analysis (PCA) for face recognition. Furthermore, this research proposed face recognition with an encryption process to protect the data stored in a database. The encryption process consists of two main processes: confusion and diffusion. The confusion process is randomizing the position of the original image pixels. This research utilized the Arnold’s cat map (ACM) for the confusion process, and the diffusion process was performed using the XOR operation with the key generated from the 1D chaos system. Three different 1D chaos systems, namely logistic map, Bernoulli map, and tent map, were compared to see which chaos system had the best encryption results. Tests were conducted by comparing various parameters on the three proposed 1D chaos systems, including correlation coefficient, histogram analysis, entropy value, number of pixel rate changes (NPCR), and unified average change intensity (UACI). Based on testing the image encryption results, the diffusion process utilizing the tent map produced the best image encryption compared to other chaotic systems.

Segmentation of hyphae and yeast in fungi-infected tissue slice images and its application in analyzing antifungal blue light therapy.

Candida albicans is a pathogenic fungus that undergoes morphological transitions between hyphal and yeast forms, adapting to diverse environmental stimuli and exhibiting distinct virulence. Existing research works on antifungal blue light (ABL) therapy have either focused solely on hyphae or neglected to differentiate between morphologies, obscuring potential differential effects. To address this gap, we established a novel dataset of 150 C. albicans-infected mouse skin tissue slice images with meticulously annotated hyphae and yeast. Eleven representative convolutional neural networks were trained and evaluated on this dataset using seven metrics to identify the optimal model for segmenting hyphae and yeast in original high pixel size images. Leveraging the segmentation results, we analyzed the differential impact of blue light on the invasion depth and density of both morphologies within the skin tissue. U-Net-BN outperformed other models in segmentation accuracy, achieving the best overall performance. While both hyphae and yeast exhibited significant reductions in invasion depth and density at the highest ABL dose (180J/cm2), only yeast was significantly inhibited at the lower dose (135J/cm2). This novel finding emphasizes the importance of developing more effective treatment strategies for both morphologies.

Increasing the Usability and Accessibility of Voyager 2 Images of Triton

Much of what we know about Neptune’s moon Triton was inferred from the analysis of images returned by the Voyager 2 mission, the only spacecraft to have visited that putative ocean world. Unfortunately, the highest-resolution images (scales < 2 km pixel−1) are difficult to use because they are only available in nonstandard formats, and the locations of the images on Triton’s surface are incorrect by up to 200 km. Although image mosaics of Triton are publicly available, these do not include the highest-resolution data. Here we describe our effort to improve the usability and accessibility of Voyager 2 images of Triton. We used the USGS’s ISIS software to process 41 Triton images, including geometric calibration, radiometric calibration, and reseau removal. We improved the image locations using a photogrammetric control network with 958 points and 3910 image measurements. Least-squares bundle adjustment of the network yielded rms uncertainty of 0.50, 0.52, and 0.51 pixels in latitude, longitude, and radius, respectively, and maximum residuals of −4.21 and +3.20 pixels, respectively. Image-to-image alignment is therefore vastly improved. We have released these processed images as cloud-optimized GeoTIFFs in orthographic projection at the original pixel scale of each image. Associated mosaics have also been created and released to provide geologic context for the individual images. These products provide the science community with analysis-ready data that enable new investigations of Triton, increase accessibility to this unique data set, and continue to enhance the scientific return from the Voyager 2 mission.

Reconfigurable Liquid Crystal Elastomer Director Patterns for Multi-Mode Shape Morphing

Liquid crystal elastomers (LCEs) are a monolithic material with programmable three-dimensional (3D) morphing modes stemming from their designable non-uniform molecular orientations (or director). However, the shape morphing mode is generally fixed when director patterns of LCEs are determined. Multi-mode shape morphing is difficult to achieve since director patterns cannot be reconfigured. Herein, we demonstrate the ability to reconfigure LCE director patterns and initial shapes—and thus shape morphing modes—by the manual assembly and de-assembly of LCE pixels. We measured the mechanical properties of LCEs with and without UV glue and found their Young’s moduli were 9.6 MPa and 11.6 MPa. We firstly fabricate LCE pixels with designed director fields and then assemble 24 pixels with required director fields into an LCE film with a designed director pattern, which corresponds to a programmed shape morphing mode. We further exhibit that we can de-assemble the LCE film back into original pixels or new pixels with different shapes and then re-assemble them into a new film with a different initial shape and director pattern, which corresponds to a second programmed shape morphing mode. Principally, we can have a large amount of shape morphing modes if we have enough pixels. The demonstrated capability of multi-mode shape morphing enhances functions of LCEs, which broadens their applications in soft robotics, programmable origami/kirigami, responsive surfaces, and so on.

An Effective Multiple-Bit Data Abstraction and Lossless Retrieval without Shifting

Cryptography and Steganography are two major forms of enforcing data privacy, with the former being prevalent. For small payloads, Steganography is preferred, as the data remains concealed and abstracted. The process involves embedding bits onto a carrier image. However, the disadvantage of steganography is that the carrier image gets distorted, which is a major concern when it comes to pixel sensitive images, such as forensic and medical images that aremission critical. Furthermore, due to modifications in the pixel values, the images get distorted, which can be irrecoverable. Hence, the technique of Reversible Data Hiding(RDH) can be used as a solution. Usually two methods are used under RDH, namely Histogram Shifting (HS) and Difference Expansion (DE). Histogram Shifting method shifts all pixel values between pairs of peak and zero points, which allows data to be embedded in the intermediate points. However, this method also depends on frequencies of pixel values on the image, andthe embedding capacity varies greatly on the image. In Difference Expansion Method, the difference between a pixel and its adjacent pixels is considered to embed a single bit intoone of the pixels. Here, a scheme to embed multiple bits in a pixel is proposed, which greatly enhances the embedding capacity. Initially, three horizontally adjacent pixels are takenand the difference between the rightmost and leftmost pixel iscalculated. This difference is used to classify the centre pixel as embeddable or non-embeddable. If the pixel is embeddable, anew pixel value is calculated, which comprises of the secret bits, offset bits and flag bits, without the use of any difference expansion or histogram shifting process. The secret bits and the original pixel value can be easily recovered from the modified value using the offset and flag bits. This scheme has higher embedding capacity, and lower complexity in terms of computations, when comparedwith other RDH schemes.

Multi‐chaotic maps and blockchain based image encryption

SummaryThe vast technological developments make data transmission more frequent over networks. So, the data needs to be secured and for that reason, there is a requirement to develop an effective encryption model. This article proposes a novel image encryption model using multi‐chaotic maps and blockchain (MCBE). Chaotic maps have been used in encryption models as chaotic maps have the properties of randomness, and non‐periodicity that get utilized in improving the efficiency of an encryption model. Logistic and tent maps have been employed as multi‐chaotic maps to make the encryption process effective in this work. A logistic map has a simple structure and is easily implementable but has some drawbacks like small keyspace. Keyspace and randomness are enhanced by using a tent map along with a logistic map. The proposed MCBE methodology applies to grayscale or colored images having different file formats and sizes. Initially, in the confusion phase, the permutation of the original image is performed based on a random permutation that changes the original image pixel's position. The encryption model applies multi‐chaotic maps, row‐wise and column‐wise in the diffusion phase to promote the efficiency of the encryption model. Finally, blockchain has been implemented using the SHA‐256 hash function to obtain the encrypted image that enhances the security by increasing the keyspace, to resist brute force attacks. The evaluation performance of the MCBE has been analyzed against various attacks namely statistical attacks, differential attacks, NIST randomness test, noise, and cropping attacks. The evaluated keyspace is which has high key sensitivity with a correlation of encrypted images close to 0. Maximum achieved entropy value of 7.9998, SSIM value between original and encrypted images nearby 0 confirms the dissimilarity. The Number of Pixels Change Rate (NPCR) value of 99.62%, and the value of Unified Average Changed Intensity (UACI) value of 33.54% are within the specified standard range. The calculated correlation coefficient, NPCR, and UACI values have been compared with the existing algorithms, and the results show that the proposed MCBE methodology has better performance than the other state‐of‐the‐art methods. The experimental results indicate that the chaotic ranges generated by multi‐chaotic maps and the SHA‐256 hash function improve the keyspace and security of the encryption model confirming the efficacy of the proposed model MCBE.

Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine

INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine. OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing. METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise. RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images. CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier. The entire system developed using python programming.

An ultra-short-term forecasting method for multivariate loads of user-level integrated energy systems in a microscopic perspective: based on multi-energy spatio-temporal coupling and dual-attention mechanism

An ultra-short-term multivariate load forecasting method under a microscopic perspective is proposed to address the characteristics of user-level integrated energy systems (UIES), which are small in scale and have large load fluctuations. Firstly, the spatio-temporal correlation of users’ energy use behavior within the UIES is analyzed, and a multivariate load input feature set in the form of a class image is constructed based on the various types of load units. Secondly, in order to maintain the feature independence and temporal integrity of each load during the feature extraction process, a deep neural network architecture with spatio-temporal coupling characteristics is designed. Among them, the multi-channel parallel convolutional neural network (MCNN) performs independent spatial feature extraction of the 2D load component pixel images at each moment in time, and feature fusion of various types of load features in high dimensional space. A bidirectional long short-term memory network (BiLSTM) is used as a feature sharing layer to perform temporal feature extraction on the fused load sequences. In addition, a spatial attention layer and a temporal attention layer are designed in this paper for the original input load pixel images and the fused load sequences, respectively, so that the model can better capture the important information. Finally, a multi-task learning approach based on the hard sharing mechanism achieves joint prediction of each load. The measured load data of a UIES is analyzed as an example to verify the superiority of the method proposed in this paper.

Reversible Data Hiding in Encrypted Images Based on Two-Round Image Interpolation

The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation in the decrypted domain. Of these mechanisms, pixel prediction is among the most frequently used, directly affecting the capacity and fidelity. In this paper, we propose a novel method that uses a two-round interpolation mechanism to enhance pixel prediction precision while preserving a large number of carrier pixels. In the proposed method, the content owner uses a stream cipher to encrypt the image as a carrier. The data hider flips specific LSBs of the encrypted image for data embedding. On the receiver side, the process of data extraction and image recovery is divided into two stages. In each stage, based on the varying distributions of the original or recovered pixels with the carrier pixels, the corresponding pixel interpolation schemes are used to accurately predict the pixels for data extraction and image recovery. The results demonstrate that the proposed method can efficiently improve the capacity and fidelity with full reversibility compared to existing VRAE RDHEI methods.

An image inpainting algorithm using exemplar matching and low-rank sparse prior

Image inpainting is a challenging problem with a wide range of applications such as object removal and old photo restoration. The methods based on low-rank sparse prior have been used for regular or nearly regular texture inpainting. However, since such inpainting results do not synthesize the original pixels, they are usually not sharp especially when the area to be recovered is large. One remedy is to use an exemplar-based method. However, it often produces false matches and cannot obtain globally consistent inpainting results. In this paper, we give a new model to promote low rankness and sparsity and solve this model with a recently proposed Riemannian optimization algorithm. Furthermore, we propose a novel two-stage algorithm by integrating the low-rank sparse model with an exemplar-based method. Numerical experiments demonstrate that the proposed low-rank sparsity-based method and the two-stage algorithm achieve encouraging results compared to state-of-the-art image completion algorithms.

An interpolation-based reversible data hiding scheme for internet of things applications

The exponential growth of the Internet of Things (IoT) has led to an increased demand for secure and efficient data transmission methods. However, there is a tradeoff in the image quality and hiding capacity in the data hiding methods. Therefore, the maximum amount of data that could be stored in the image media is a difficult challenge while maintaining the image quality. Thus, to make the balance between the quality of the images and the embedding capacity, a novel interpolation-based revisable data hiding (RDH) approach is developed for IoT applications. The proposed interpolation technique takes the average of the root value for the product of two neighboring original pixel values and the third original pixel value. And for the central pixel, it takes an average of two interpolated pixels. By doing so, most of the original pixels are considered and the calculated interpolated pixel is much enhanced as its average value. Furthermore, the data hiding is performed in two stages. In the first stage, RSA is performed on the secret message, and then embedding is done based on which intensity range group. The experimental results indicate that the proposed technique enhanced the embedding capacity by 17.58% and produced 7.80% higher PSNR values for the test images as compared to the baseline methods.

Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.

Self-supervised-RCNN for medical image segmentation with limited data annotation.

Many successful methods developed for medical image analysis based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To overcome the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled imaging data is proposed in this work. For the pretraining, different distortions are arbitrarily applied to random areas of unlabeled images. Next, a Mask-RCNN architecture is trained to localize the distortion location and recover the original image pixels. This pretrained model is assumed to gain knowledge of the relevant texture in the images from the self-supervised pretraining on unlabeled imaging data. This provides a good basis for fine-tuning the model to segment the structure of interest using a limited amount of labeled training data. The effectiveness of the proposed method in different pretraining and fine-tuning scenarios was evaluated based on the Osteoarthritis Initiative dataset with the aim of segmenting effusions in MRI datasets of the knee. Applying the proposed self-supervised pretraining method improved the Dice score by up to 18% compared to training the models using only the limited annotated data. The proposed self-supervised learning approach can be applied to many other medical image analysis tasks including anomaly detection, segmentation, and classification.

Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm.

The recovery of semantics from corrupted images is a significant challenge in image processing. Noise can obscure features, interfere with accurate analysis, and bias results. To address this issue, the Regularized Neighborhood Pixel Similarity Wavelet algorithm (PixSimWave) was developed for denoising Nifti (magnetic resonance imaging (MRI)). The PixSimWave algorithm uses regularized pixel similarity detection to improve the accuracy of noise reduction by creating patches to analyze the intensity of pixels and locate matching pixels, as well as adaptive neighborhood filtering to estimate noisy pixel values by allocating each pixel a weight based on its similarity. The wavelet transform breaks down the image into scales and orientations, allowing a sparse image representation to allocate a soft threshold on its similarity to the original pixels. The proposed method was evaluated on simulated and raw T1w MRIs, outperforming other methods in terms of an SSIM value of 0.9908 for a low Rician noise level of 3% and 0.9881 for a high noise level of 17%. The addition of Gaussian noise improved PSNR and SSIM, with the results indicating that the proposed method outperformed other models while preserving edges and textures. In summary, the PixSimWave algorithm is a viable noise-elimination approach that employs both sparse wavelet coefficients and regularized similarity with decreased computation time, improving the accuracy of noise reduction in images.

Reliable and Secure Data Transfer in IoT Networks using Knight-Tour and PHLSB Method

Data security has recently become the most pressing problem for corporate owners and even regular people due to the continual development in the speed of data transmission over the entire Internet, and stricter regulations have been made for data protection. Secure key management is now essential for securing information exchange due to the Internet of Things (IoT) and the rapidly advancing mobile device technologies. Smart home and healthcare IoT apps, for instance, offer automated services to users with little user involvement. Data transmission delays and intrusions can affect existing security solutions that use a single link. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The methods Knight-Tour and polynomial-based hash least significant bit (PHLSB) that take use of flaws in the human visual system (HVS) are combined to form a hybrid approach to image steganography in this research. The Knight's trip path is a chess board layout that follows the route of a horse without stopping at any of the nodes again. In order to create the scrambled image, this travel path pattern is employed to permute the original image's pixel positions. Prior to being masked behind a cover image, this technique makes sure the communication has been encrypted. A strong mathematical tool is a polynomial equation. The cover object's data was hidden using these equations as a secret key. The suggested solution efficiently secures IoT devices by assigning the resource-intensive encryption processing to local organisations. As a result of the proposed framework's simulation findings, network lifetime PDR (Packet Drop Ratio) has improved, throughput has increased, energy consumption has decreased, and latency has decreased.

A new paradigm for high-capacity reversible data hiding with pixel repetition and adaptive embedding

Reversible data hiding (RDH) offers a useful attribute of retaining the visual semantics with security properties, for the effective management and protection of digital images with their metadata. This paper introduces a Reversible Data Hiding (RDH) scheme with Pixel Repetition (PR) to selectively embed additional secret data. bits utilizing the image redundancy created with PR. A generalized framework of PR is first developed and the rate–distortion criterion is extended for adaptive embedding. An input image is defined to be scaled-up by replacing each original pixel (called seed) with a block of shape s×s containing the seed, where s is the scaling factor. For better utilizing this PR-based image redundancy, we then propose an RDH scheme with two-phase embedding for s=2. The first phase of the scheme embeds 4-bit data in each 2 × 2 image block using a mapping function, which is designed to modify the repeated pixels, keeping the seed intact in each block. Being invertible, the mapping function also allows another phase of embedding in the preserved seeds using an adaptive selection of their least significant bits. Compared to the prominent PR-based and other related schemes, an impressive rate–distortion performance of the proposed scheme is recorded with a greater potential for the applications that require a high and varying embedding rate with better image quality.