A Fast Image Compression Algorithm Based on Wavelet Transform

With multimedia becoming widely popular, the conflict between mass data and finite memory devices has been continuously intensified; so, it requires more convenient, efficient and high-quality transmission and storage technology and meanwhile, this is also the researchers’ pursuit for highly efficient compression technology and it is the fast image transmission that is what people really seek. This paper mainly further studies wavelet analysis and fractal compression coding, proposes a fast image compression coding method based on wavelet transform and fractal theory, and provides the theoretical basis and specific operational approaches for the algorithm. It makes use of the smoothness of wavelet, the high compression ratio of fractal compression coding and the high quality of reconstructed image. It firstly processes the image through wavelet transform. Then it introduces fractal features and classifies the image according to the features of image sub-blocks. Each class selects the proper features. In this way, for any sub-block, it only needs to search the best-matched block in a certain class according to the corresponding features. With this method, it can effectively narrow the search in order to speed up coding and build the relation of inequality between the sub-block and the matching mean square error. So, it can effectively combine wavelet transform with fractal theory and further improves the quality of reconstructed image. By comparing the simulation experiment, it objectively analyzes the performance of algorithm and proves that the proposed algorithm has higher efficiency.

IMPROVEMENT OF METHODS AND ALGORITHM OF FRACTAL COMPRESSION OF A GRAPHIC IMAGE

During the intensive development of information systems, the volumes of data necessary for storing and processing information, in particular graphic images, grow. One of the reasons for this phenomenon is the desire to continually improve the quality of the content we receive. The ever-growing demand for image quality requires the development of new and improvement of existing approaches to information compression. Compression algorithms use the presence of so-called surplus in the data, which can be eliminated during data storage and restored during playback. Currently in demand methods are based on storing only low-frequency components. Such methods are used in JPEG, MPEG compression algorithms. The disadvantage of such algorithms is not a large compression ratio. In this regard, methods have emerged based on fractal data compression. The main idea of ​​the method is to store the image as affine transformations, which leads to its compression. The paper proposes preliminary processing of graphic data for storing them in the form of a file with the * .json extension. The application of a further fractal compression algorithm to the already obtained file allows to reduce the processing time of data, computational calculations. The resulting file will have the advantages of fractal compression such as decompression speed, better compression ratio and higher resolution compared to * .jpeg and * .bmp. Despite all the advantages, fractal compression of graphic information is used quite rarely. This is due to the complexity of the algorithm, the lack of a sufficient number of specialists on this issue and the estimate of the licensed software. The improvement is aimed at simplifying the algorithm and its implementation will avoid these drawbacks and expand the scope of fractal compression.

Review of video compression techniques based on fractal transform function and swarm intelligence

Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information. Where textual data processing focuses on a structured or unstructured way of data processing which computes in less time with no compression over the data, multimedia data are processing deals with a processing requirement algorithm where compression is needed. This involve processing of video and their frames and compression in short forms such that the fast processing of storage as well as the access can be performed. There are different ways of performing compression, such as fractal compression, wavelet transform, compressive sensing, contractive transformation and other ways. One way of performing such a compression is working with the high frequency component of multimedia data. One of the most recent topics is fractal transformation which follows the block symmetry and archives high compression ratio. Yet, there are limitations such as working with speed and its cost while performing proper encoding and decoding using fractal compression. Swarm optimization and other related algorithms make it usable along with fractal compression function. In this paper, we review multiple algorithms in the field of fractal-based video compression and swarm intelligence for problems of optimization.

A NEW APPROACH OF FRACTAL COMPRESSION USING COLOR IMAGE

Fractal image compression depends on self-similarity, where one segment of a image is like the other one segment of a similar picture. Fractal coding is constantly connected to grey level images. The simplest technique to encode a color image by gray- scale fractal image coding algorithm is to part the RGB color image into three Channels - red, green and blue, and compress them independently by regarding each color segment as a specific gray-scale image. The colorimetric association of RGB color pictures is examined through the calculation of the relationship essential of their three-dimensional histogram. For normal color images, as a typical conduct, the connection necessary is found to pursue a power law, with a non- integer exponent type of a given image. This conduct recognizes a fractal or multiscale self-comparable sharing of the colors contained, in average characteristic pictures. This finding of a conceivable fractal structure in the colorimetric association of regular images complement other fractal properties recently saw in their spatial association. Such fractal colorimetric properties might be useful to the characterization and demonstrating of natural images, and may add to advance in vision. The outcomes got demonstrate that the fractal-based compression for the color image fills in similarly with respect to the color image.