@article{Gordani_Simoni_2024, title={Leveraging SVD for efficient image compression and robust digital watermarking}, volume={4}, url={https://publish.mersin.edu.tr/index.php/ades/article/view/1496}, abstractNote={<p>In terms of digital files, compression is the act of encoding information using fewer bits than what’s found in the original file. When we say image compression, we have in mind an image that has fewer bytes than the original image but has the most important features that describe the original image. So, the aim of image compression is to reduce the image size without degrading image quality below an acceptable threshold. In MATLAB, an image is stored as a matrix. One approach is to apply the Singular Values Decomposition (SVD) to the image matrix. This method is implemented in MATLAB. In order to divide the matrix of the given image into three other matrices in MATLAB, we can use the function <em>svd()</em>. As performance metrics, we can use PSNR and Compression ratio. Digital Watermarking is defined as the process of hiding a piece of digital data in the cover data which is to be protected and extracted later for ownership verification. In an SVD-based watermarking scheme, the singular values of the cover image are modified to embed the watermark data. All tests and experiments are performed using MATLAB as the computing environment and programming language. Also, in the RStudio programming language we can see the implementation of the SVD method in image compression.</p>}, journal={Advanced Engineering Science}, author={Gordani, Ornela and Simoni, Aurora}, year={2024}, month={Sep.}, pages={103–112} }