Video Quality
This lecture overviews Video Quality that has many applications in cinema movies, digital TV and video streaming. It covers the following topics in detail: Video Quality Assessment Methods. Subjective Quality Assessment: MOS, DMOS and Preference factor (PF). Objective Quality Assessment Metrics: Psychophysical Metrics, Engineering Metrics and Methods (PEVQ, Pixel – based Metrics, VMAF). Camera image quality in cameras.
Moving Image Perception
This lecture overviews Moving Image Perception that has many applications in video acquisition, processing and coding. It covers the following topics in detail: Human Vision Modeling. Video Frequency Content, (spatial, temporal and spatiotemporal frequencies). Spatiotemporal HVS Models (Kelly experiments, spatiotemporal contrast sensitivity, saccadic movements, Smooth eye pursuit movement). Video Quality Assessment.
Video Digitization
This lecture overviews Video Digitization that has many applications in digital video and TV. It covers the following topics in detail: Video scanning and sampling. 3D data types (Digital video signal, volumetric images). Progressive/Interlaced video sampling. 2D sampled image spectrum. Reconstruction of analog video. General video sampling grids: interlaced, quincunx, orthorhombic grids. General analog video sampling, spectrum and reconstruction.
Introduction to Video Processing and Analysis
This lecture overviews Video Processing and Analysis that has many applications in digital TV, video streaming, video conferencing and social media, to mention a few applications. It covers the following topics in detail: Video sampling and digitization, Visual Moving Image Perception, Video filtering, Motion Estimation 2D visual object tracking, Video Compression, Video indexing and retrieval, Video Summarization, Video Captioning, Video description.
Hidden Markov Models
This lecture overviews Hidden Markov Models that have many applications in Data Analytics and Signal Analysis. It covers the following topics in detail: Markov Chains. Hidden Markov Chains: Viterbi algorithm, Forward-backward algorithm. HMMs applications: Speech recognition, Name-entity recognition, Human Action Recognition, Gesture recognition.
Spectral Signal Analysis
This lecture overviews Spectral Signal Analysis that has many applications in periodicity estimation and acoustic/speech/music/biomedical signa analysis. It covers the following topics in detail: Power Spectrum, autocorrelation function and Fourier Transform. Power Spectrum and random signals. Bartlett Method, Welch Method Blackman – Tukey Method for Power Spectrum estimation.