The lecture gives an introduction to the basics of image and video signal processing. First, point operations, morphological filters, and color spaces including tri-chromaticity are explained. Subsequently, the theory of multidimensional signals and systems is introduced and Wiener filtering for image signals is derived. Based on this, interpolation methods for images such as bicubic and spline interpolation are explained. This is followed by methods for feature detection in images using Hough transforms and edge detection, and the principle of scale-invariant features is explained. For video signals, motion estimation methods such as optical flow and image matching algorithms using SIFT and SURF are explained. Finally, the theory of image and Video segmentation using statistical methods is introduced, and transform-based methods for image processing are presented.