Fast Fractal Coding Research Articles

Research on adaptive fractal image compression algorithm based on flexible classification technology

This paper proposes a flexible classification technique for accelerating image matching and improving coding quality, using a fast adaptive fractal image compression method with four fork tree as image division and flexible classification technology, which is a kind of pre-segmentation of images. With using the self-similarity of each part of the image, the fractal encoding is carried out. In order to reduce the encoding time to dozens of seconds, it eliminate the block effect and realize the fast fractal coding of color image, so as to speed up the encoding speed and further improve the image comprehensive performance of compression ratio and signal-to-noise ratio/encoding time.

A Fast Fractal Coding Method for Image with Primary Additional Errors

Today, in the multimedia encoding technology, fractal image coding is an effective coding method without resolution. The effectiveness is because of the high compressing ratio of fractal image coding. But the computational complexity of this coding method is so high that it needs long encoding time. In this paper, a novel fast fractal coding method is constructed to decrease the coding time by the capture of primary additional error values. This method is a universal algorithm, which is independent of image types. First, we abstract the additional error values from classic image coding. Then, we present a method to abstract the primary error values with a given rule of weight. Moreover, the encoding and decoding processes are reformed to store the primary additional error values. Finally, experimental results shows the improved fractal image coding method has higher compressing ratio and better effectiveness (signal to noise ratio) than the classic algorithm.

Self-embedding fragile watermarking based on DCT and fast fractal coding

A self-embedding fragile watermarking scheme is proposed in this paper, which is based on Discrete Cosine Transform and fractal compression coding. To overcome the high computational complexity of fractal coding, a fast coding method is also presented that improves the efficiency of fractal block coding in the watermarking procedure. In our algorithm, three kinds of watermarks are generated for image authentication and recovery, which is based on an interleaved and overlapped 8 ×8 image block structure. This makes our method obtain an authentication granularity of 4 ×4 approximately. At the same time, we take advantage of two levels of mapping to select mapping block for every image block. Three versions of recovery watermarks for each block are embedded in different quadrants, which provides another two chances for block recovery in case one is destroyed. Experimental results demonstrate that the proposed scheme not only outperforms conventional self-embedding fragile watermarking algorithms in tamper recovery, but also improves the security against the various counterfeiting attacks.

A new approach in road sign recognition based on fast fractal coding

The tasks of traffic signs are notifying drivers about the current state of the road and giving them other important information for navigation. In this paper, a new approach for detection, tracking, and recognition such objects is presented. Road signs are detected using color thresholding, after that candidate blobs that have specific criteria are classified based on their geometrical shape and are tracked trough successive frames based on a new similarity measure. Candidate blobs that successfully pass the tracking module are processed for extracting their fractal features, and final recognition is done based on support vector machines with kernel function. Results validate effectiveness of newly employed fractal feature and show high accuracy with a low false hit rate of this method and its robustness to illumination changes and road sign occlusion or scale changes. Also results indicate that compared to the other pictogram feature representation techniques, this approach shows a more proper description of road signs.

A FAST FRACTAL ENCODING METHOD BASED ON FRACTAL DIMENSION

In this paper a fast fractal coding method based on fractal dimension is proposed. Image texture is an important content in image analysis and processing which can be used to describe the extent of irregular surface. The fractal dimension in fractal theory can be used to describe the image texture, and it is the same with the human visual system. The higher the fractal dimension, the rougher the surface of the corresponding graph, and vice versa. Therefore in this paper a fast fractal encoding method based on fractal dimension is proposed. During the encoding process, using the fractal dimension of the image, all blocks of the given image first are defined into three classes. Then each range block searches the best match in the corresponding class. The method is based on differential box counting which is chosen specifically for texture analysis. Since the searching space is reduced and the classification operation is simple and computationally efficient, the encoding speed is improved and the quality of the decoded image is preserved. Experiments show that compared with the full search method, the proposed method greatly reduced the encoding time, obtained a rather good retrieved image, and achieved the stable speedup ratio.

An efficient fractal image-coding method using interblock correlation search

An efficient fractal image-coding method that exploits the correlation between range blocks is proposed. Four domain blocks mapped by the precious neighboring range blocks are considered as the good candidate blocks of the input range block. Experimental results show that when the proposed method is implemented with a fast fractal coding algorithm, it can further reduce the encoding time and bit rate with insignificant loss of image quality.

Low-complexity fractal-based image compression using two-stage search strategy

A fractal image compression scheme is presented which incorporates the vector quantizer (VQ) to reduce the computational complexity in the encoding phase. The high-detail blocks of an input image are approximated by a fast fractal coding, and the low-detail blocks are encoded by the VQ technique. In fractal image coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. We propose a two-stage search algorithm to reduce the search space of the domain pool. First, a fast and simple binary pattern matching algorithm is proposed to extract a small pool of candidate domains, and then the gray-level-matching algorithm is used to find the best match from the candidates. In addition, a variable block-size segmentation method is used in our coding system to further improve the image quality. It is shown that the average encoding time is short, and the average speedup factor of 45 is achieved, as compared to the full search fractal coding. Simulation results also show that the proposed scheme can achieve better image quality and higher compression ratio than other fast fractal-based coding schemes.