M.E. Computer Science & Engineering:Digital Speech and Image ProcessingThapar University
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Advanced Data Structures
Software Design and Construction
Software Engineering Concepts and Methodologies
Advanced Computer Architecture
Parallel and Distributed Computing
Advanced Database Systems
Soft Computing Neural Networks
Digital Speech and Image Processing
Speech Representation techniques: Statistical model for speech, STFT, Design of digital filter banks, Analysis by synthesis, cepstrum, pitch detection, Spectral and non-spectral analysis techniques; Model-based coding techniques;
Speech signal processing: Different coding techniques, Noise reduction and echo cancellation; Synthetic and coded speech quality assessment; Selection of recognition unit; Model-based recognition; Language modeling; Speaker Identification; Text analysis and text-to-speech synthesis
Image representation and modeling: Fourier transform, z- transform, optical and modulation transfer functions, Matrix theory results, block matrices, Random signals, Discrete random fields, spectral density functions, results from estimation theory.
Image Perception: Light, luminance, brightness and contrast, MTF of Visual system, Visibility function, Monochrome vision methods, Image fidelity criteria, color matching and reproduction, color coordinate systems, color difference measures, color vision model, Temporal properties of vision.
Image Sampling & Quantization: Introduction, two dimensional sampling theory, Extensions of sampling theory, Practical limitations in sampling and reconstruction, Image Quantization, Optimum mean square or lloyd Max quantizer, A compandor design.
Image Transform: Two dimensional orthogonal and unitary transforms, properties of unitary transforms, Two dimensional DFT, Cosine transform, KL-transform.
Image Representation by Stochastic Models: Introduction, One dimensional causal models, One dimensional Spectral Factorization, AR Models, linear prediction in two dimension, Image decomposition, Fast KL transforms.
Image Enhancement: Point Operations, Spatial Operations, Transform Operations, Multispectral Image Enhancement, False Color and pseudocolor, color image enhancement.
Image Filtering and Restoration: Introduction, Image observation models, Inverse and Wiener filtering, FIR Wiener filters, Fourier domain filters, filtering using image transforms, Smoothing splines and Interpolation, least square filters, Generalized inverse, SVD and Iterative methods, Recursive filtering for state variable system, causal models, Semi-causal models, Digital processing of speckle images, Maximum entropy restoration, Bayesian methods.