Explore MATLAB source code curated for "图像压缩" with clean implementations, documentation, and examples.
This method primarily utilizes wavelet transform for image compression and evaluates compression performance by calculating key metrics including compression ratio and signal-to-noise ratio (SNR) of the compressed image.
Source code for implementing BP neural network in image compression applications - available for download with detailed technical documentation.
Comprehensive wavelet application examples document covering denoising, image compression, edge distortion, image fusion, wavelet packet decomposition and reconstruction, accompanied by detailed MATLAB implementation code with algorithm explanations.
This code implements a dual-density dual-tree complex wavelet transform algorithm, providing effective solutions for image denoising and image compression. The source code demonstrates excellent implementation quality with robust signal processing capabilities.
Image Compressive Sensing Using Dual-Tree Wavelet Transform and Universal Hidden Markov Tree Modeling Approach
Contains images with code implementation. The code is straightforward, primarily utilizing principal component analysis with key functions for dimensionality reduction and data reconstruction.
Implementation of image compression based on Singular Value Decomposition, providing code examples for both grayscale and RGB true-color image compression with algorithmic explanations.
MATLAB implementation of image compression and decompression following the JPEG2000 standard with detailed code analysis
Application Background: Image transmission in wireless sensor networks (WSNs) presents significant challenges in resource optimization. By improving processing techniques and reducing energy consumption through memory-efficient image compression algorithms, we can enable diverse applications. Key Technology: Image compression techniques are employed to decrease processor bandwidth utilization, minimize energy consumption during processing, and optimize image transmission efficiency - with implementations often involving discrete cosine transform (DCT) or wavelet-based compression algorithms.
Implementation of SPIHT image compression algorithm based on wavelet and bandelet transforms with enhanced code-level explanations