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Lecture
Image Processing II: Optimal Transforms and Applications
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Related lectures (28)
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JPEG 2000: Image Compression
Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.
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Covers advanced topics in watermarking, including resisting scaling and rotations, self-referenced watermarking, and types of attacks.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Image Processing II: Coding and Compression
Explores image coding, compression techniques, and wavelet-based methods for efficient data representation.
Orthogonality and Projection
Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.
Orthogonality and Gram-Schmidt Process
Explores orthogonality, Gram-Schmidt process, dot products, and solution minimization in systems.
Principal Component Analysis: Applications and Limitations
Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.
PCA: Directions of Largest Variance
Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.
Image Processing I
Introduces the basics of image processing, covering point and spatial operators, histogram processing, and transform-domain operations.
Dirac's Notation, Tensor Product
Covers Dirac's notation in linear algebra and the tensor product concept in Hilbert spaces.
