8x8 Matrix DCT Transformation with Quantization, Dequantization, and Inverse DCT Processing
Implementation of 8x8 matrix DCT transformation, quantization, inverse quantization, and inverse DCT transformation for image compression applications
MATLAB Implementation of Discrete Cosine Transform (DCT)
This code demonstrates DCT transformation using MATLAB, showing signal processing effects through frequency domain conversion with dct() function implementation.
Digital Watermarking Algorithm Based on DCT Transformation
Source code implementation of a DCT-based digital watermarking algorithm, providing valuable reference material for students and researchers studying digital watermarking technologies. The implementation includes core processing functions and detailed comments demonstrating practical embedding and extraction techniques.
Image Compression Using DCT Transformation: Methods for Grayscale and True-Color Images
Implementation of DCT-based image compression techniques with separate approaches for grayscale and true-color images, including code implementation details and compression performance analysis.
DCT-Based Image Compression Source Code
MATLAB implementation of DCT-based image compression algorithm that divides images into 8x8 blocks, performs DCT transformation and quantization, then reconstructs the compressed image with comprehensive technical documentation and code explanations.
Image Transformation with DCT and IDCT Implementation
Image transformation techniques involving 8x8 block partitioning, Discrete Cosine Transform (DCT) for compression encoding, and Inverse DCT (IDCT) for decompression, with code implementation insights
Digital Watermark Processing System Based on DCT Watermarking Algorithm
A DCT-based watermarking algorithm digital watermark processing system primarily consists of two processes: watermark embedding and watermark detection. The watermark embedding process combines the digital watermark (X), original image (I), and key/public key (K) through a digital watermark embedding algorithm to generate the watermarked image (I*). The watermark detection process uses the digital watermark (X), watermarked image (I*), and key/public key (K) through a digital watermark detection algorithm to detect the presence or absence of the watermark. The DCT-based watermarking algorithm embeds a random sequence as the watermark signal into the magnitude components of significant DCT coefficients (selecting the top n coefficients with large magnitudes as significant components). A similarity function is defined to verify successful watermark extraction. Experimental results demonstrate the algorithm's imperceptibility and robustness against common image processing operations.
Performing 8x8 Block DCT Transformation on Images
Implementing 8x8 block Discrete Cosine Transform (DCT) for image processing and using inverse DCT to restore original images from 8x8 blocks
MATLAB Implementation of Image Compression Encoding with DCT, Quantization, and Run-Length Encoding
Image compression encoding implementation using Discrete Cosine Transform (DCT), quantization techniques, zigzag scanning, and run-length encoding for efficient image storage and transmission
Image Compression Using FFT, DCT, HT, Wavelet Transforms and Vector Quantization Techniques
MATLAB implementation of image compression based on Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Hadamard Transform (HT), Wavelet Transform and Vector Quantization techniques. Implementation includes transform domain processing, coefficient quantization, and entropy coding modules.