Digital Image and Video Processing Essentials

This module introduces images and videos as 2D and 3D signals, discussing their analog/digital dichotomy and characteristics across the electromagnetic spectrum.

This module covers fundamentals of 2D signals and systems, including complex exponential signals, linear space-invariant systems, 2D convolution, and filtering in the spatial domain.

This module explores 2D signals in the frequency domain, covering 2D Fourier transform, sampling, discrete Fourier transform, and filtering in the frequency domain.

This module covers motion estimation and color representation and processing, including applications, phase correlation, block matching, spatio-temporal gradient methods, and color image processing.

This module focuses on image and video enhancement techniques, including intensity transformation, histogram processing, noise smoothing, sharpening, and video enhancement.

This module covers image and video recovery techniques, including image restoration, inverse filtering, constrained least squares, and iterative restoration algorithms.

This module explores stochastic approaches to image and video recovery, including Wiener filters, maximum likelihood estimation, and Bayesian restoration algorithms.

This module introduces image and video compression with a focus on lossless techniques, covering information theory, Huffman coding, run-length coding, arithmetic coding, and predictive coding.

This module covers lossy image compression techniques, including scalar and vector quantization, differential pulse-code modulation, fractal compression, transform coding, JPEG, and subband compression.

This module focuses on video compression, covering motion-compensated hybrid video encoding and various video compression standards including H.264, H.265, and MPEG series.

This module introduces image and video segmentation techniques, including methods based on intensity discontinuity and similarity, watersheds, K-means algorithms, and advanced methods.

This module introduces the concept of sparsity and its applications in image and video processing, covering sparsity-promoting norms, matching pursuit algorithm, and smooth reformulations.