Grayscale Image Compression Using DCT Transformation
Implementing grayscale image compression through DCT transformation, calculating compression ratios, and comparing images with different compression levels using MATLAB-based algorithms.
Image Compression Using Matrix Singular Value Decomposition
This method compresses images through matrix singular value decomposition by first dividing the image into blocks, computing inner products of corresponding pixels within each block to obtain correlation matrix A, performing SVD on A, selecting n (n<N) dominant eigenvectors corresponding to larger eigenvalues, and using inner products between these vectors and image blocks for compression and reconstruction.
Applications of Wavelet Transform in Image Processing
Wavelet Transform applications in image processing include: image format conversion between different types, 2D wavelet denoising, 2D wavelet image compression, wavelet analysis for noisy image denoising, 2D wavelet smoothing using median filtering, wavelet-based image enhancement, and wavelet analysis for image fusion of two distinct images. Implementation typically involves MATLAB's Wavelet Toolbox functions like wavedec2 for decomposition and waverec2 for reconstruction.
Generation and Compression of Linear Frequency-Modulated Radar Echoes using Dechirp Processing
Generation and Compression of Linear Frequency-Modulated Radar Echoes through Dechirp Processing Technique
Various Methods for Synthetic Aperture Radar Image Processing
Different approaches for Synthetic Aperture Radar image processing, including speckle noise removal algorithms, image segmentation techniques, compression methods, and denoising processes. Provides implementation insights and algorithm explanations for technical reference.
Two-Dimensional Grayscale Image Processing Using Haar Wavelet Transform and EZW Algorithm
For an input two-dimensional grayscale image, this process first applies the lifting Haar wavelet transform, then compresses the wavelet coefficients using the classic EZW algorithm, and finally reconstructs the original image through inverse transformation. The implementation involves key steps including wavelet decomposition, coefficient quantization, and hierarchical encoding for efficient compression.
Wavelet Coefficient Extraction and Zerotree Encoding for Image Compression
Implementation of HAAR wavelet transforms for wavelet coefficient relationship extraction, followed by zerotree encoding for compression and subsequent decompression to reconstruct the original image. Contains minor optimizations that serve as valuable references for similar implementations.
Two-Dimensional Image Compression Using Discrete Cosine Transform
Implementation of image compression techniques through MATLAB programming experiments, focusing on Discrete Cosine Transform (DCT) algorithm optimization and parameter analysis.
Hyperspectral Image Compression Using Inter-band Prediction
Implementation of hyperspectral image compression through inter-band prediction techniques, including unidirectional and bidirectional prediction methods with optimal reference band selection strategies
Robust Watermarking in the DCT Domain
Robust DCT-domain watermarking that withstands compression, cropping, and other common attacks