Data Redundancy Removal Using DCT Transform

This document presents an image compression algorithm comprising three sequential processing stages:

1. Initial phase employs Discrete Cosine Transform (DCT) to convert image blocks from spatial to frequency domain, effectively separating visual information from redundancy through coefficient concentration in upper-left matrix regions.

2. Subsequent quantization stage utilizes customizable quantization tables to scale DCT coefficients, where higher frequency components undergo stronger compression via division with larger quantization values - implemented through element-wise matrix operations between DCT coefficients and quantization tables.

3. Final stage applies Huffman entropy coding to quantized coefficients, generating variable-length codes optimized for statistical frequency distributions, with implementation involving probability calculation, binary tree construction, and code assignment routines.

The algorithmic implementation features modular architecture with three components: main control program managing workflow sequencing, plus two specialized subroutines for DCT quantization (handling block processing and matrix operations) and Huffman encoding (managing statistical analysis and bitstream generation). This approach effectively compresses image files to conserve storage capacity and enhance transmission efficiency.