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Abstract
In this research, a new technique is suggested to reduce the long time required by the encoding process by using modified moment features on domain blocks. The modified moment features were used in accelerating the matching step of the Iterated Function System (IFS). The main disadvantage facing the fractal image compression (FIC) method is the over-long encoding time needed for checking all domain blocks and choosing the least error to get the best matched domain for each block of ranges. In this paper, we develop a method that can reduce the encoding time of FIC by reducing the size of the domain pool based on the moment features of domain blocks, followed by a comparison with threshold (the selected threshold based on experience is 0.0001). The experiment was conducted on three images with size of 512x512 pixel, resolution of 8 bits/pixel, and different block size (4x4, 8x8 and, 16x16 pixels). The resulted encoding time (ET) values achieved by the proposed method were 41.53, 39.06, and 38.16 sec, respectively, for boat , butterfly, and house images of block size 4x4 pixel. These values were compared with those obtained by the traditional algorithm for the same images with the same block size, which were 1073.85, 1102.66, and 1084.92 sec, respectively. The results imply that the proposed algorithm could remarkably reduce the ET of the images in comparison with the traditional algorithm.
Highlights
A new image compression technique, called fractal compression, has become rapidly popular.Nowadays it is being used in many implementations, including texture segmentation, image compression, image signatures, feature extraction, and image watermarking [1]
Our experimental results were based on the comparison of the traditional fractal image compression algorithm and the proposed method based on the moment technique
We observed that the highest encoding time (ET) value of boat image using the proposed method was 41.53sec, while that obtained using the traditional algorithm
Summary
A new image compression technique, called fractal compression, has become rapidly popular. Nowadays it is being used in many implementations, including texture segmentation, image compression, image signatures, feature extraction, and image watermarking [1]. FIC is utilized in various image processing applications, such as image retrieval, feature extraction, image signature, multi-resolution medical image processing, and texture segmentation [3]. It has important advantages, such as high compression ratio and fast image reconstruction. This problem could be solved through an effective technique that reduces the size of the domain pool based on the moment features of domain blocks, followed by a comparison with a threshold value before the matching process, so as to make the encoding process faster [5]
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