分数盒维数的分形图像编码改进算法

Fractal image coding improved algorithm based on fractional box-counting dimension

分形图像编码是一种很有前途的限失真压缩方法,它具有好的视觉质量和高压缩比,然而,固有的编码耗时限制了它的应用范围.针对这个问题,提出了一个限制搜索空间的算法.具体地说,它通过分析分数盒维数与图像子块纹理特征的关系,依据图像子块的分数盒维数,将子块分成三类,不同类采用不同的方法寻找分形码;同时,引入误差阈值来控制搜索邻域的大小.对Lena等五幅测试图像的实验结果显示,与基本算法相比,本文改进算法的编码速度平均加快了24倍,以PSNR度量的解码图像质量平均提高了0.25dB.

Fractal image coding is a promising lossy compression technique in terms of achievable compression ratios and decoded image quality;however,it has the primary disadvantage of high computational demands resulting in unacceptably long encoding times.This paper thus proposes an accelerating scheme by discussing their relation between the texture feature of image blocks and fractional box-counting dimension.Depending on fractional box-counting dimension,the image blocks are divided into three categories,and different coding methods are employed.Experimental results on five popular 512×512 test images show that,compared to the baseline fractal algorithm with the full search,the proposed algorithm not only can achieve the speed-up of about 24 times,but also can increase 0.25dB of the decoded image quality.