基于相关系数的快速分形图像编码算法的改进

分形图像编码具有快速解码的优点，但需要较长的编码时间。因此，快速编码算法对扩大分形编码的应用领域是十分必要的。最近，作者提出了一种基于相关系数的快速分形编码算法，该算法基于一个未经理论证明的命题（两个等尺寸的子块不能组成匹配对，除非它们的相关系数相对较大）。该文继续讨论基于相关系数的快速分形编码算法，从理论上验证了该算法依据的命题，并改进了这个算法。计算机仿真显示，与基本分形算法比较，改进的相关系数算法能够实现加快编码5倍左右，同时峰值信噪比（PSNR）还有所增加。

Improvement of Fast Algorithm Based on Correlation Coefficients for Fractal Image Encoding

Fractal image coding has the advantage of very fast decoding, but it suffers from long encoding time. Therefore, it is necessary to develop fast encoding algorithms before it could be widely used for various applications. Recently, the authors presented a correlation coefficient-based algorithm for fast fractal image encoding, which is based on the theoretically-unproved proposition that two equal-sized image blocks cannot be closely matched unless their correlation coefficient is relatively large. This paper pursues the correlation coefficient-based algorithm to verify theoretically the above proposition and improve the original algorithm. The simulation results show that, in comparison to the corresponding baseline fractal algorithm, the improved algorithm can achieve the speed-up of about 5 times while it gives a little increase of the PSNR (peak signal-to-noise ratio).