In this paper, a method is proposed to increase the compression ratio for the color images by
dividing the image into non-overlapping blocks and applying different compression ratio for these
blocks depending on the importance information of the block. In the region that contain important
information the compression ratio is reduced to prevent loss of the information, while in the
smoothness region which has not important information, high compression ratio is used .The
proposed method shows better results when compared with classical methods(wavelet and DCT).

In this paper the behavior of the quality of the gradient that implemented on an image as a function of noise error is presented. The cross correlation coefficient (ccc) between the derivative of the original image before and after introducing noise error shows dramatic decline compared with the corresponding images before taking derivatives. Mathematical equations have been constructed to control the relation between (ccc) and the noise parameter.

In this paper, an adaptive medical image watermarking technique is proposed based on wavelet transform and properties of human visual system in order to maintain the authentication of medical images. Watermark embedding process is carried out by transforming the medical image into wavelet domain and then adaptive thresholding is computed to determine the suitable locations to hide the watermark in the image coefficients. The watermark data is embedded in the coefficients that are less sensitive into the human visual system in order to achieve the fidelity of medical image. Experimental results show that the degradation by embedding the watermark is too small to be visualized. Also, the proposed adaptive watermarking technique can preserv

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Like the digital watermark, which has been highlighted in previous studies, the quantum watermark aims to protect the copyright of any image and to validate its ownership using visible or invisible logos embedded in the cover image. In this paper, we propose a method to include an image logo in a cover image based on quantum fields, where a certain amount of texture is encapsulated to encode the logo image before it is included in the cover image. The method also involves transforming wavelets such as Haar base transformation and geometric transformation. These combination methods achieve a high degree of security and robustness for watermarking technology. The digital results obtained from the experiment show that the values of Peak Sig

In this paper, an algorithm through which we can embed more data than the
regular methods under spatial domain is introduced. We compressed the secret data
using Huffman coding and then this compressed data is embedded using laplacian
sharpening method.
We used Laplace filters to determine the effective hiding places, then based on
threshold value we found the places with the highest values acquired from these filters
for embedding the watermark. In this work our aim is increasing the capacity of
information which is to be embedded by using Huffman code and at the same time
increasing the security of the algorithm by hiding data in the places that have highest
values of edges and less noticeable.
The perform

In this paper, a simple medical image compression technique is proposed, that based on utilizing the residual of autoregressive model (AR) along with bit-plane slicing (BPS) to exploit the spatial redundancy efficiently. The results showed that the compression performance of the proposed techniques is improved about twice on average compared to the traditional autoregressive, along with preserving the image quality due to considering the significant layers only of high image contribution effects.

      The image caption is the process of adding an explicit, coherent description to the contents of the image. This is done by using the latest deep learning techniques, which include computer vision and natural language processing, to understand the contents of the image and give it an appropriate caption. Multiple datasets suitable for many applications have been proposed. The biggest challenge for researchers with natural language processing is that the datasets are incompatible with all languages. The researchers worked on translating the most famous English data sets with Google Translate to understand the content of the images in their mother tongue. In this paper, the proposed review aims to enhance the understanding o

    Glaucoma is one of the most dangerous eye diseases. It occurs as a result of an imbalance in the drainage and flow of the retinal fluid. Consequently, intraocular pressure is generated, which is a significant risk factor for glaucoma. Intraocular pressure causes progressive damage to the optic nerve head, thus leading to vision loss in the advanced stages. Glaucoma does not give any signs of disease in the early stages, so it is called "the Silent Thief of Sight". Therefore, early diagnosis and treatment of retinal eye disease is extremely important to prevent vision loss. Many articles aim to analyze fundus retinal images and diagnose glaucoma. This review can be used as a guideline to help diagnose glaucoma. It presents 63 artic