Using DCT and Quadtree for Image Compression

Abstract

Data file compression has been used for a long time to reduce data file sizes, due to restriction in memory capacity and slow network communication speeds. Compression is also applied to images and became a necessity with the introduction of the world wide web since images are used extensively in web sites. A Bitmap is a standard for image storing introduced by Microsoft for its Windows operating system. Bitmaps are very popular today, but they use a lot of memory space, giving the need for compressing them. Various techniques are used for bitmap (BMP) image compression. Several are lossless, that is the quality of the image is not modified, and others are lossy, in which a part of the image is lost. Examples of lossless techniques include GIF and PNG, while JPG is an example of a lossy technique. JPG is composed of Discrete Cosine Transform (DCT), Quantization, Huffman coding and Runlength encoding (RLE). Quadtrees are also used for lossless or lossy image compression. In this work, we propose an algorithm based on DCT/Quantization and Quadtrees to be used in sequence together for lossy image compression. Our proposed method will be compared with other techniques, namely JPEG and Quadtree with different parameters. In the results, our proposed algorithm performed well compared to other quadtree methods.