Image Compression Processing in MATLAB with Source Code Implementation
MATLAB source code for image compression processing, featuring algorithm implementations and practical applications
Source Encoding for Image Compression at Transmitter and Source Decoding for Image Reconstruction at Receiver
This MATLAB implementation demonstrates image compression via source encoding at the transmitter and image reconstruction through source decoding at the receiver, utilizing JPEG standard based on DCT transform. The project includes noise addition to test images followed by denoising, compression, and reconstruction to analyze compression performance. It explores downsampling and interpolation-based compression algorithms, with potential extensions from 8-bit/pixel grayscale to 24-bit/pixel color images or implementation of lossless compression techniques.
Image Compression
Spatial coherence often exists between color values of sampling points across an image surface - for instance, the identical coloration of two mice, the background wall, and gray floor. These uniform color blocks can be effectively compressed through algorithms like run-length encoding or discrete cosine transform.
MATLAB Implementation of Shannon-Fano Encoding and Decoding for Image Compression
This MATLAB program implements Shannon-Fano encoding and decoding algorithms for efficient compression and decompression of binary and color images, featuring probability-based symbol sorting and recursive tree generation for optimal prefix codes.
Image Compression Using 9/7 Wavelet Transform
MATLAB simulation of image compression using 9/7 wavelet transform, demonstrating high compression ratios through experimental validation
Implementation of JPEG2000 in MATLAB - The Latest Image Compression Standard
MATLAB Implementation of JPEG2000 with Code-Level Algorithm Explanations and Technical Applications
Huffman Coding for Image Compression and Decompression
Huffman coding is highly effective for image compression and decompression, suitable for processing various types of images with demonstrated practical utility in reducing file sizes and improving transmission efficiency.
Neural Network-Based Image Compression Implementation
This code implements image compression using neural networks with convolutional architectures and deep learning techniques for efficient image data reduction.
Image Compression Using Singular Value Decomposition
Implementing image compression via singular Value Decomposition helps beginners understand practical applications of SVD with code implementation insights and algorithm explanations
Fundamental Principles and Characteristics of Singular Value Decomposition
Based on the fundamental principles and characteristics of Singular Value Decomposition (SVD), this article presents a method for image compression using SVD. The method is illustrated through simple examples demonstrating the basic compression process and workflow. Practical image processing implemented through MATLAB programming validates the effectiveness of the approach, with detailed explanations of key functions like svd() for matrix decomposition and imshow() for image visualization.