Introduction Compression technique has created an increasing important to enhance image storage and transmission. Due to redundancy and similarity across distinct parts in an image, compression is achievable. In these types of situations, fractal compression of images is a highly effective form of compression. Fractals are geometric or rough shapes that can be divided into portions, each of which is a smaller version of the whole due to a property known as self-similarity. Fractals can be

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... d into portions because they are self-similar to one another [1]. A mathematical fractal is based on an equation that is iterated repeatedly and is a type of recursive feedback. A common occurrence in nature is fractals. Across a wide but finite scale range, these artifacts have a self-similar structure. Nature's fractals, such as trees and ferns, may be computer-modeled using a recursive process [18] . In the early 1990s, Jaquin proposed using block forming as a basis for a fractal approach, and this idea has since been widely adopted. When it comes to compressing images, coder usage of square chunks known as "range" is widely employed. A domain block is four times as large as a range block in size. Once the image has been divided into smaller square blocks, those blocks are given the range blocks [2] . The search engine looks for the domain blocks and their rotations that have the best matches for each range block. Every range block is saved, along with the domain and any other relevant information to obtain that range [16] . As a result, rather than storing the entire range, only the parameters are retained, which results in compression. The decoder reconstructs the original image through iterative procedures. Now because of the high compression ratio fractal compression is particularly helpful, the algorithm's decoding step is unaffected by the reconstructing image, and the reconstruction of an image is of high quality [17] . Using the difference between the two images, the peak signal-to-noise ratio (PSNR) is computed to determine the quality of the reconstructed image compared to the original image [7] . A B S T R A C T Fractal compression is compression of the lossy type, which is applied for natural textured images. A fractal image compression technique based on the Quadtree algorithm and Huffman coding is proposed in this work. Agonizingly, the term "fractal compression" refers to an image compression technique that uses the fractal geometry of the image data stream to achieve lossy compression. Realistic images and textures are created with the help of this tool. It is based on the fact that parts of an image are frequently similar to other parts of the same image, which allows for faster processing. The most widely used partitioning mechanism is image partitioning in a tree structure. In this emerging world of image processing, quadtree partitioning is a one-of-a-kind technique that divides an image into a set of homogeneous regions. Huffman coding is a type of data compression that is lossless. The Huffman encoding algorithm is introduced through this technique, which creates an alphabetic list of all of the alphabet symbols, which is then arranged in descending order of their likelihood of occurring. The peak signal-to-noise ratio is quite improved by employing the proposed technique, and the encoding time is reduced.