For some images which have less structure-similarity, the reconstructed image quality of FIC is usually unacceptable. The other main bottleneck FIC technology facing is the image quality problem.
For example, the APCC-based method only takes 0.0766 seconds to encode a 512 × 512 image on a home computer with CPU I3–2100 and windows XP OS, and the peak signal to noise ratio (PSNR) of the reconstructed image only degrades about 0.3 to 0.4 dB than that of the baseline fractal image compression (BFIC). Based on the fact that the affine similarity in FIC is equivalent to Pearson’s absolute correlation, we proposed the absolute value of Pearson’s correlation coefficient (APCC) -based accelerating algorithms, which significantly speed up the encoding phase of FIC while preserving the reconstructed image quality well. The overlong encoding time is an important problem of FIC, and most of the publications about FIC are focused on this topic. One bottleneck is the overlong encoding time, and the other is that the image quality of the reconstructed images for some images with low structural similarity is usually unacceptable. To date, FIC is still one of the research hotspots in image processing.Īlthough great progress had been made, FIC suffered from two important bottlenecks in the past two decades. Aside from the application as an image compression technology, FIC has been widely used in some other fields, such as image denoising, image encryption, image sharpening, and facial image recognition. Since the original fractal image compression scheme was proposed by Barnsley and Jacquin, FIC technology had demonstrated rapid development. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC.įractal image compression (FIC) is an image coding technology based on the local affine similarity of image structure. Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. We call it the sparse fractal image compression (SFIC). In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. Based on the absolute value of Pearson’s correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC.
Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. First, the encoding phase of FIC is time-consuming. However, two main bottlenecks restrained the develop and application of FIC for a long time. As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms.