摘要
目的将混沌应用于分形图像压缩编码中，用Logistic混沌映射和Julia曲线生成一个固定的压缩字典，改进传统的分形图像压缩编码方法．方法采用二阶的Julia集f（Z）：Z^2＋C的时间逃逸算法。对于不同的C生成不同的曲线。然后使用Logistic混沌映射随机地产生0-255之间的整数填满量化表．再根据灰度量化规则，用第一千张量化表量化产生的Julia图像缺，作为压缩编码中的固定字典、编码时，将量化后图像Julia块与原图中的图像缺进行比较，寻找最适合的量化表和距离最小的Julia图像块．解码时通过重构第一千张量化表来重建原图像、结果与传统的分形压缩编码相比较．该方法能生成丰富且固定的压缩字典，编码的速度快，解码后的图像质量高．结论用Logistic混沌映射产生的随机数序列作为量化表中的系数，并用固定的压缩字典来取代变化的压缩字典，通用性强，编码时间少，实验证明，本算法切实可行．压缩效果好．
The paper is done in order to apply the Logistic mapping to fractal image compressing so that the traditional means of fractal imagecompresslon can be improved, By making use of the time escaping arith- metic of Julia collecting F（Z） = Z^2 ＋ C, different curves corresponding to different C are established. Via using Logistic mapping chaos mapping function to create random integers between 0-255 to fill quantized table. After that according to the grey-scale quantized formula, the one thousandth quantized table is used to quantize the Julia image block to form the stationary dictionary. By comparing the quantized Julia image block with the block from the original image, a block whose distance is the minimum in Hausdorff measurement is chosen and its corresponding fractal parameter is saved. In decoding, the one thousandth quantized table is reconstructed to rebuild the original image. Compared with the traditional fractal coding method, this way not only can get abundant and fixed dictionary but also the course of decoding is very fast and the rebuilt image is of high quality. Through using Logistic mapping function as well as replacing the variable compressing dictionary with a fixed one, it is universal and has a high coding speed. The experiment has proved that this method is very feasible and can obtain very good result.
出处
《沈阳建筑大学学报（自然科学版）》
EI
CAS
2006年第6期995-998,共4页
Journal of Shenyang Jianzhu University：Natural Science
基金
沈阳市科技局(1022038-1-04)
