Image Compressor

1. Overview

This project implements a basic graphical interface that simulates an image compression system based on the JPEG standard using Python, OpenCV, and PyQt5.

2. Flow Diagram

2.1 Encoding Process

2.2 Decoding Process

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3. System Description

3.1 Encoding

The encoding process consists of the following steps:

• The input image is divided into small 8×8 blocks to reduce computational complexity.
• Since the image dimensions may not be divisible by 8, zero-padding is applied to ensure complete 8×8 blocks.
• Each 8×8 block is transformed using the Discrete Cosine Transform (DCT).
• The transformed blocks are combined to obtain the DCT-transformed image.
• Quantization is performed using a quantization table and the zigzag scanning algorithm.
• The resulting matrices are converted into one-dimensional arrays.
• Data compression is applied using Run-Length Encoding (RLE) or Huffman coding.

3.2 Decoding

The decoding process includes the following steps:

• The compressed data is decoded to reconstruct the quantized coefficients.
• Inverse zigzag scanning is applied to restore the 8×8 block structure.
• Dequantization is performed using the quantization table.
• Each block undergoes Inverse DCT (IDCT).
• The blocks are merged to reconstruct the original image.
• Padding pixels are removed to recover the original image size.

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4. Graphical User Interface

Some screenshots of the implemented graphical interface are shown below:

Figure 1: Main Interface

Figure 2: Compression Interface