Description: Image quality assessment is the process of analyzing and measuring the quality of an image based on various criteria, such as clarity, contrast, color fidelity, and the absence of artifacts. This process is crucial in various applications, from image compression to video transmission, where visual quality can affect user experience. In the context of convolutional neural networks (CNNs), image quality assessment has become even more sophisticated. CNNs are deep learning models that have proven to be highly effective in image processing tasks, and their ability to learn complex features from images allows them to perform more accurate evaluations. Image quality can be assessed subjectively, through human observers’ opinions, or objectively, using computational metrics that quantify specific aspects of the image. Objective metrics include Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), which allow for comparisons between original images and their processed or compressed versions. Image quality assessment is essential to ensure that computer vision systems and artificial intelligence applications produce visual results that are not only accurate but also aesthetically pleasing.
