The Multimedia Project consists of two sections that focus on image processing and image search based on color histograms.

The first section of the Multimedia Project involves the implementation of various quantization image algorithms. These algorithms are applied to images to achieve color reduction and improve storage efficiency. The following algorithms are implemented:

• Median Cut: The Median Cut algorithm divides the color space into smaller cubes and selects representative colors for each cube, resulting in reduced color complexity.
• K-means: The K-means algorithm clusters colors into K groups based on their similarity, allowing for color reduction while preserving image quality.
• Floyd Steinberg: The Floyd Steinberg algorithm is an error diffusion dithering technique that distributes quantization errors to neighboring pixels, resulting in visually pleasing images with reduced color complexity.
• Octree: The Octree algorithm constructs an octree data structure to efficiently represent colors in an image, enabling color reduction and efficient storage.

The second section of the Multimedia Project focuses on image search based on color histograms. After applying the quantization algorithms from Section 1 to the images, the project aims to find similar images within a specific folder. This is achieved by comparing the color histograms of the images using histogram-based similarity metrics.

• Quantization Image Algorithms: The project implements Median Cut, K-means, Floyd Steinberg, and Octree algorithms for color quantization, reducing the complexity of images while maintaining visual quality.
• Color Histogram Comparison: The project uses color histograms to compare images and determine their similarity based on histogram-based similarity metrics.
• Image Search: Given a specific folder of provided images, the project enables searching for similar images by comparing color histograms.
• Image Processing: The project performs image processing tasks, including color quantization and histogram generation, to facilitate image search and analysis.
• User Interface: The project may include a user-friendly interface allowing users to input images, select algorithms, and visualize results.