High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data.This property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation.However,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations.In addition,these numbers may cluster in certain ranges.The hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can detect.Therefore,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image.This paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data concealment.The AVL tree algorithm provides several advantages for image steganography.Firstly,it ensures balanced tree structures,which leads to efficient data retrieval and insertion operations.Secondly,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image quality.The data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel extraction.The green channel serves as the foundation for building a balanced binary tree.First,the sender identifies the colored cover image and secret data.The sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated information.The next step is to create a balanced binary tree based on the green channel.Utilizing the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue channel.The first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret data.After embedding the bits in the binary tree level by level,the model restores the AVL tree to create the stego image.Ultimately,the receiver receives this stego image through the public channel,enabling secret data recovery without stego or crypto keys.This method ensures that the stego image appears unsuspicious to potential attackers.Without an extraction algorithm,a third party cannot extract the original secret information from an intercepted stego image.Experimental results showed high levels of imperceptibility and security.

Elevating Image Steganography:A Fusion of MSB Matching and LSB Substitution for Enhanced Concealment Capabilities

In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential priority.To overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure communications.Most of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB substitution.In this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)substitution.The proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three bits.The proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment capacity.The achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 dB.These findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.

Paired regions for shadow removal approach based on multi-features

The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challenge of shadow removal is how to restore the content of shadow areas correctly while removing the shadow in the image.Paired regions for shadow removal approach based on multi-features is proposed, in which shadow removal is only performed on related sunlit areas.Feature distance between regions is calculated to find the optimal paired regions with considering of multi-features(texture, gradient feature, etc.) comprehensively.Images in different scenes with peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) evaluation indexes are chosen for experiments.The results are shown with six existing comparison methods by visual and quantitative assessments, which verified that the proposed method shows excellent shadow removal effect, the brightness, color of the removed shadow area, and the surrounding non-shadow area can be naturally fused.

P2P网络多视点立体视频传输的数据块调度策略

立体视频数据量大、编码结构复杂,因此视频数据块的重要性和紧急程度对视频的播放质量有更大的影响作用。综合考虑立体视频播放质量和网络传输效率,提出一种联合优先级和随机传输的P2P数据块调度算法。依据数据块的重要性、紧急性和稀有性计算传输优先级别,同时评估邻居节点传输能力,利用多余带宽传输随机数据块。该算法能够提供优越的视频播放质量,并能够使数据块均匀分布,保证系统传输性能。仿真实验通过分析视频PSNR、启动延迟、节点吞吐量和数据到达率等指标,证明了该算法的有效性。

一种自适应滤波分形图像压缩方法

在基本分形图像压缩方法的基础上 ,结合DCT提出了一种自适应滤波分形图像压缩方法 ,该方法根据图像的能量分布调整滤波器 ,能有效地减少图像的整体匹配误差 ,提高信噪比 .实验表明 。

Local binary pattern-based reversible data hiding

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

Efficient Rate Control Algorithm for Hierarchical Video Coding

For rate control (RC) of hierarchical structure coding, an independent rate-quantization (R-Q) model was proposed based on mean absolute differences (MADs) in different temporal levels (TLs). In the proposed R-Q model, a novel MAD model was developed according to the hierarchical structure. The experimental results demonstrate that the proposed algorithm provides better performance, in terms of average peak signal-to-noise ratio (PSNR) and quality smoothness, than the H.264 reference model, JM14.2, under various sequences.

A New Approach Based on Set Partitioning in Hierarchical Tree Algorithm for Image Compression

In this paper, the authors propose a new approach to image compression based on the principle of Set Partitioning in Hierarchical Tree algorithm （SPIHT）. Our approach, the modified SPIHT （MSPIHT）, distributes entropy differently than SPIHT and also optimizes the coding. This approach can produce results that are a significant improvement on the Peak Signal-to-Noise Ratio （PSNR） and compression ratio obtained by SPIHT algorithm, without affecting the computing time. These results are also comparable with those obtained using the Embedded Zerotree Wavelet （EZW） and Joint Photographic Experts Group 2000 （JPG2） algorithms.

Optimization of Demosaicing Algorithm for Autofluorescence Imaging System

Autofluorescence imaging(AFI) systems are widely used in the detection of precancerous lesions.Fluorescence images of precancerous tissue are usually red(R) or blue(B), so this kind of system has high requirement for colour recovery, especially in R and B channels. Besides, AFI system requires bulk data transmission with no time delay. Existing colour recovery algorithms focus more on green(G) channel, overlooking R and B channels. Although the state-of-art demosaicing algorithms can perform well in colour recovery, they often have high computational cost and high hardware requirements. We propose an efficient interpolation algorithm with low complexity to solve the problem. When calculating R and B channel values, we innovatively propose the diagonal direction to select the interpolation direction, and apply colour difference law to make full use of the correlation between colour channels. The experimental results show that the peak signal-to-noise ratios(PSNRs)of G, R and B channels reach 37.54, 37.40 and 38.22 dB, respectively, which shows good performance in recovery of R and B channels. In conclusion, the algorithm proposed in this paper can be used as an alternative to the existing demosaicing algorithms for AFI system.