Explore MATLAB source code curated for "图像压缩" with clean implementations, documentation, and examples.
MATLAB-based JPEG2000 image compression implementation using Discrete Wavelet Transform (DWT). Modify the image path in the code to run the program and observe compression results, with detailed explanations of the implementation approach and key functions.
In a MATLAB environment, a numerical simulation study was conducted using the db10 wavelet from the Daubechies wavelet family to compress speckle-degraded images. The research demonstrates that both threshold selection and decomposition levels significantly impact image compression quality. The implementation involves wavelet decomposition, thresholding operations, and reconstruction algorithms to analyze compression performance.
A comprehensive Contourlet Transform toolkit including directional filter bank decomposition and reconstruction modules, suitable for image compression, image fusion, remote sensing image reconstruction, and other applications. The code is executable and produces high-frequency images with different decomposition levels through multiscale directional decomposition algorithms.
MATLAB-based image compression implementation using wavelet transforms, featuring detailed explanations for each subroutine including key algorithm implementations and function descriptions.
This article explores image compression using Linear Predictive Coding (LPC), a lossless compression technique with code implementation insights
Wavelet analysis represents a sophisticated branch of signal processing where wavelet transforms enable critical applications including image compression, vibration signal decomposition and reconstruction. Compared to Fourier transforms, wavelet transformations operate as local transforms in both spatial and frequency domains, allowing efficient information extraction from signals. Through fundamental operations like scaling and translation, wavelet transforms achieve multi-scale signal decomposition and reconstruction, effectively overcoming many limitations of Fourier analysis. As a new mathematical discipline, wavelet analysis synthesizes functional analysis, Fourier analysis, and numerical analysis, serving as a powerful "time-scale" analysis and multi-resolution analysis technique with extensive applications across signal processing, speech synthesis, image compression, and pattern recognition.
MATLAB program for image compression using wavelet transform
This experiment performs image compression on the raw grayscale image Lena_gray.bmp by applying 8×8 DCT transformation. We compare results after keeping only the DC coefficient, and retaining the first 4, 8, and 16 transform coefficients (DC+AC) followed by IDCT reconstruction. Implementation can be achieved using MATLAB's Image Processing Toolbox functions with appropriate block processing and coefficient thresholding techniques.
Classic MATLAB implementation using wavedec2, appcoef2, and detcoef2 functions to decompose images, extract high/low frequency coefficients, and perform image compression and reconstruction with complete source code.
In JPEG compression, an image is divided into 8x8 pixel blocks for sub-block compression using discrete cosine transform and quantization techniques.