Optimized Hybrid DCT-SVD Computation over Extremely Large Images

High performance computing (HPC) is required for image processing especially for picture element (pixel) with huge size. To avoid dependence to HPC equipment which is very expensive to be provided, the soft approach has been performed in this work. Actually, both hard and soft methods offer similar goal which are to reach time computation as short as possible. The discrete cosine transformation (DCT) and singular values decomposition (SVD) are conventionally performed to original image by consider it as a single matrix. This will result in computational burden for images with huge pixel. To overcome this problem, the second order matrix has been performed as block matrix to be applied on the original image which delivers the DCT-SVD hybrid formula. Hybrid here means the only required parameter shown in formula is intensity of the original pixel as the DCT and SVD formula has been merged in derivation. Result shows that when using Lena as original image, time computation of the singular values using the hybrid formula is almost two seconds faster than the conventional. Instead of pushing hard to provide the equipment, it is possible to overcome computational problem due to the size simply by using the proposed formula.

An Image Encryption Technique Based on Logistic Sine Map and an Encrypted Image Retrieval Using DCT Frequency

In this digital era, most of the hospitals and medical labs are storing and sharing their medical data using third party cloud platforms for saving maintenance cost and storage and also to access data from anywhere. The cloud platform is not entirely a trusted party as the data is under the control of cloud service providers, which results in privacy leaks so that the data is to be encrypted while uploading into the cloud. The data can be used for diagnosis and analysis, for that the similar images to be retrieved as per the need of the doctor. In this paper, we propose an algorithm that uses discrete cosine transformation frequency and logistic sine map to encrypt an image, and the feature vector is computed on the encrypted image. The encrypted images are transferred to the cloud picture database, and feature vectors are uploaded to the feature database. Pearson’s Correlation Coefficient is calculated on the feature vector and is used as a measure to retrieve similar images. From the investigation outcomes, we can get an inference that this algorithm can resist against predictable attacks and geometric attacks with strong robustness.

Improved Coefficient Recovery and Its Application for Rewritable Data Embedding

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512×512.

Detection of Outdated Structural Priors in the Discrete Cosine Transformation-based Electrical Impedance Tomography Algorithm

Morphological prior information incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm can improve the interpretability of EIT reconstructions in clinical applications. However, an outdated structural prior can yield a misleading reconstruction compromising the accuracy of the clinical diagnosis and the appropriate treatment decision. In this contribution, we propose a redistribution index scaled between 0 and 1 to quantify the possible error in a DCT-based EIT reconstruction influenced by structural prior information. Two simulation models of different tissue atelectasis and collapsed ratios were investigated. Outdated and updated structural prior information were applied to obtain different EIT reconstructions using this simulated data, with which the redistribution index was calculated and compared. When the difference between prior and reality (the redistribution index) became larger and exceeded a threshold, this was considered as an indicator of an outdated prior information. The evaluation result shows the potential of the redistribution index to detect outdated prior information in a DCT-based EIT algorithm.

Pharmacy Impact for Distinguishing Normal Face from Abnormal Face Due to COVID- 19

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. In the recent scenario, the entire globe is facing enormous health risks occurred due to Covid-19. To fight against this deadly disease, consumption of drugs is essential. Consumption of drugs may provide some abnormalities to human face. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. To assess these human face abnormalities, the application of computer vision is favoured in this study. This work analyses an input image of human’s frontal face and performs a segregation method to separate the abnormal faces. In this research work, a method has been proposed that can detect normal or abnormal faces from a frontal input image due to COVID-19. This method has used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

Normal and Abnormal Human Face Detection Based on DCT and FFT Techniques - A Proposed Method

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

Secure Data Transfer over Internet Using Image Steganography: Review

Whether it's for work or personal well-being, keeping secrets or private information has become part of our everyday existence. Therefore, several researchers acquire an entire focus on secure transmitting secret information. Confidential information is collectively referred to as Steganography for inconspicuous digital media such as video, audio, and images. In disguising information, Steganography plays a significant role. Traditional Steganography faces a further concern of discovery as steganalysis develops. The safety of present steganographic technologies thus has to be improved. In this research, some of the techniques that have been used to hide information inside images have been reviewed. According to the hiding domain, these techniques can be divided into two main parts: The spatial Domain and Transform Domain. In this paper, three methods for each Domain have been chosen to be studied and evaluated. These are; Least Significant Bit (LSB), Pixel Value Difference (PVD), Exploiting Modification Direction (EMD), contourlet transform, Discrete Wavelet Transformation (DWT), and, Discrete Cosine Transformation (DCT). Finally, the best results that have been obtained in terms of higher PSNR, Capacity, and more robustness and security are discussed.

Streaming video processing of marine scenes using advanced processor instructions

Современные полупроводниковые технологии позволяют перейти к более развитым системам видеонаблюдения, где преобразование и обработка видеоинформации выполняются непосредственно на этапе съемки и формирования видеопотока. Умные камеры расширяют функциональность встроенных видеосенсоров, обеспечивая параллельную высокоуровневую обработку видео. В предлагаемом исследовании проведена разработка адаптивных алгоритмов спектрального преобразования изображений морских сюжетов, позволяющие решить необходимые задачи в реальном времени.Актуальным становится решение задачи использования современных процессорных технологий с использованием последних достижений в архитектуре процессорного ядра, в частности расширенные SSE-инструкции. Рассмотрен математический аппарат реализации адаптивных алгоритмов дискретного косинусного преобразования на базе SSEархитектуры процессорного ядра. Предложенные алгоритмы динамически выполняют предварительный анализ движения и определяют оптимальные размеры видеокубов. Для оценки эффективности предложенных алгоритмов сжатия было использовано множество различных изображений морских судов, полученных с камер и расположенных на беспилотниках с высоты 100-400м. Показаны результаты моделирования предложенных алгоритмов обработки видеоинформации морских сюжетов и определены количественные оценки информационных показателей качества видеосистем кодирования и декодирования изображений. Modern semiconductor technologies allow us to move to more advanced video surveillance systems, where the transformation and processing of video information is performed directly at the stage of shooting and forming a video stream. Smart cameras even extend the functionality of the built-in video sensors, providing parallel high-level video processing. In the proposed study, adaptive algorithms for spectral transformation of images of marine plots were developed, which allow solving the necessary problems in real time. The solution of the problem of using modern processor technologies using the latest achievements in the architecture of the processor core, in particular, advanced SSE instructions, becomes relevant. The mathematical apparatus for implementing adaptive algorithms for discrete cosine transformation based on the SSE architecture of the processor core is considered. Proposed algorithms dynamically perform preliminary motion analysis and determine optimal dimensions of video cubes. To evaluate the effectiveness of the proposed compression algorithms, many different images of naval vessels obtained from cameras and located on drones from a height of 100-400m were used. The results of modeling the proposed algorithms for processing video information of marine scenes are shown and quantitative estimates of information quality indicators of video systems for encoding and decoding images are determined.

Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.