基于质心特征和重要敏感区域分类的分形图像压缩算法

图像压缩是数据传输和存储中必不可少的过程,分形图像压缩方法因其压缩方法简单、可任意尺度下重构、解码速度快且压缩比高具有独特优势,但传统分形图像压缩方法存在编码时间过长的缺陷。针对压缩比和恢复效果之间的不平衡问题,在确保图像恢复效果前提下,需要解决编码时间过长的问题。因此,提出了一种基于质心特征和重要敏感区域分类的分形图像压缩算法,通过构造质心特征,将基本分形算法中R块在码本中搜索最小均方误差MSE的问题转换为利用质心特征码本寻找最佳匹配块的问题,简化了块搜索过程,将全局搜索变为局部搜索,同时对重要敏感区域采取全局搜索的方式,以增强恢复图像的视觉效果。实验仿真结果表明,质心特征方法可以有效缩短编码时间,在保证图像恢复效果前提下,本文所提算法相较于基本算法最高可以节省大约64%的编码时间,相较于双交叉和特征方法,可以达到更好的恢复效果。

A fractal image compression algorithm based on centroid features and important sensitive area classification

Image compression is an indispensable process in data transmission and storage. The fractal image compression method has unique advantages due to its simple compression method, reconstruction at any scale, fast decoding speed and high compression ratio. However, the traditional fractal image compression method has a defect that the encoding time is too long. Aiming at the imbalance between the compression ratio and the recovery effect, it is necessary to solve the problem of too long time in the encoding process under the premise of ensuring the image restoration effect. So a fractal image compression method based on centroid feature and important sensitive area classification is proposed. By constructing the centroid feature, the problem of searching the minimum Mean Square Error (MSE) of the block in the basic fractal is converted into the problem of searching the best matching block of the block centroid feature in the corresponding block centroid feature codebook. It simplifies the block search process, changes a global search to a local search, considers the important sensitive area of the image, and adopts a global search for important sensitive areas, thereby increasing the visual effect of the restored image. Experimental simulation shows that, compared with the basic fractal image compression algorithm, the centroid feature method can effectively shorten the coding time. Under the premise of achieving a satisfactory image restoration effect, this method can save the coding time by about 64% compared with the basic algorithm. This method can achieve better recovery effect than the sum of double cross/eigenvalues methods.