超低位速率分形灰度图像压缩算法仿真
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摘要
超低位速率分形灰度图像压缩算法影响图像存储、图像传输和医疗远程等技术的发展,在海洋、军事、农业等方面也广泛应用。针对当前算法由于完整度不够,导致分形灰度图像压缩不到最小,且还存在图像失真和传送速率慢的问题,提出一种基于灰色关联度字典构造的超低位速率分形灰度图像压缩算法,采用小波系数对超低位速率分形灰度图像的压缩初值和差值进行分解计算。将图像矩阵分为若干小矩阵,利用EC-SVD算法控制图像矩阵压缩后的高清度误差,衡量原始图像和压缩后图像的差异,提高分形灰度图像的清晰度,降低失真效果,使图像高清且完整。仿真结果表明,所提算法对分形灰度图像的压缩较完整,使文件可以压缩到最小,提升了图像的高清度和传送速率,提高了超低位速率分形灰度图像压缩实用性。
This article puts forward an algorithm to compress ultra-low velocity fractal grayscale image based on dictionary construction of grey relational grade. Firstly,the wavelet coefficient was used to decompose and calculate the initial value and difference value of ultra-low velocity fractal grayscale image. Then,the image matrix was divided into several small matrices. Moreover,EC-SVD algorithm was used to control the high-definition error of image matrix after compression and measure the difference between original image and compressed image. Finally,definition of fractal grayscale image was improved and the distortion effect was reduced. Thus,high-definition and complete image could be obtained. Simulation results show that the proposed algorithm can compresses the fractal grayscale image completely and can compress the file to the minimum. Meanwhile,it enhances the definition and transmission rate of image and improves the practicability of ultra-low velocity fractal grayscale image compression.
This article puts forward an algorithm to compress ultra-low velocity fractal grayscale image based on dictionary construction of grey relational grade. Firstly,the wavelet coefficient was used to decompose and calculate the initial value and difference value of ultra-low velocity fractal grayscale image. Then,the image matrix was divided into several small matrices. Moreover,EC-SVD algorithm was used to control the high-definition error of image matrix after compression and measure the difference between original image and compressed image. Finally,definition of fractal grayscale image was improved and the distortion effect was reduced. Thus,high-definition and complete image could be obtained. Simulation results show that the proposed algorithm can compresses the fractal grayscale image completely and can compress the file to the minimum. Meanwhile,it enhances the definition and transmission rate of image and improves the practicability of ultra-low velocity fractal grayscale image compression.
引文
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[2]陈占良,等.高速图像压缩系统中DDR3控制器的实现[J].电光与控制,2016,(8):85-88.
[3]宋利敏,等.嵌入式无线传感器压缩图像特征提取优化仿真[J].计算机仿真,2017,(1):344-347.
[4]杨新锋,胡旭诺,粘永健.基于分类的高光谱图像压缩算法[J].红外与激光工程,2016,45(2):263-266.
[5]陈亚雄,黄樟灿,冯磊.基于奇异值分解和Contourlet变换的图像压缩算法[J].计算机应用研究,2017,34(1):317-320.
[6]马名浪,等.基于自适应稀疏变换的指纹图像压缩[J].自动化学报,2016,42(8):1274-1284.
[7]梅家诚,王瑞,叶汉民.结合稀疏表示与图像压缩融合的目标检测[J].红外技术,2016,38(3):218-224.
[8]唐国维,等.基于分形维的小波包EBCOT岩心图像压缩[J].计算机科学,2016,43(11):317-321.
[9]王柯俨,等.基于配准和二次码率控制的光谱图像压缩[J].电子科技大学学报,2017,46(1):9-14.
[10]姚军财.一种结合人眼对比度感知特性的彩色图像压缩算法[J].液晶与显示,2016,31(6):584-594.