基于对称性与方差的快速分形人脸图像压缩

针对人脸具有对称性特点，提出了基于对称性与方差的快速分形人脸图像压缩算法。在编码时，对某值域块，将定义域块限定在与其对称的候选区内，如在其中未找到匹配定义域块，则再扩大候选区范围。对基于方差方法与本文算法在时间复杂性上的分析表明，即使在最坏情况下，利用本算法所需编码时间也仅为基于方差方法的1／2。通过在ORL和YALE人脸库中的实验表明，利用本文算法，在基本保持恢复图像质量的同时，平均编码时间仅为基于方差方法的1／3。实验还讨论了候选区、阈值与击中值域块个数、编码时间和峰值信噪比（PSNR）间的关系。

The Fast Fractal Face Image Compression Based on Symmetry and Variance

The fast fractal face image compression algorithm is proposed based on the facial symmetry. In the encoding, domain blocks are restricted in the symmetrical candidate region for the range block. If no match blocks can be found, the candidate region is expanded. We analyze the time complexity of the variance based method and the proposed method. The result shows that the encoding time of the proposed method is half of the variance based method even under the worst circumstance. The experiments on the ORL and YALE face databases show that the average encoding time is 1/3 of the variance based method when using the proposed method. The relationship among the candidate region, threshold, hit range blocks,encoding time and PSNR is discussed by the experiments.