In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020†software to evaluate the performance of the proposed system.
Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)
... Show MoreOptic Disc (OD) localization is a basic step for the screening, identification and appreciation of the risk of diverse ophthalmic pathologies such as glaucoma and diabetic retinopathy.In fact, the fundamental step towards an exact OD segmentation process is the success of OD localization. This paper proposes a fully automatic procedure for OD localization based on two of the OD most relevant features of high-intensity value and vasculature convergence. Merging ofthese two features renders the proposed method capable of localizing the OD within the variously complicated environments such as the faint disc boundary, unbalanced shading, and the existence of retinal pathologies like cotton wall and exudates,which usually share the same
... Show MoreModeling the microclimate of a greenhouse located in Baghdad under its weather conditions to calculate the heating and cooling loads by computer simulation. Solar collectors with a V-corrugated absorber plate and an auxiliary heat source were used as a heating system. A rotary silica gel desiccant dehumidifier, a sensible heat exchanger, and an evaporative cooler were added to the collectors to form an open-cycle solar assisted desiccant cooling system. A dynamic model was adopted to predict the inside air and the soil surface temperatures of the greenhouse. These temperatures are used to predict the greenhouse heating and cooling loads through an energy balance method which takes into account the soil heat gain. This is not included in
... Show MoreOur goal in this work is to describe the structure of a class of bimodal self maps on the compact real interval I with zero topological entropy and transitive.
Fuzzy Based Clustering for Grayscale Image Steganalysis
Laser is a powerful device that has a wide range of applications in fields ranging from materials science and manufacturing to medicine and fibre optic communications. One remarkable
When images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensiona
... Show More