分块自适应纸箱图像压缩方法研究
BLOCKWISE ADAPTIVE CARTON IMAGE COMPRESSION ALGORITHM
张宝 1谭建 2杨涛2
作者信息
- 1. 四川中烟工业有限责任公司 四川 绵阳 621000
- 2. 西南科技大学信息工程学院 四川 绵阳 621010
- 折叠
摘要
针对纸箱等结构清晰图像提出一种基于字典学习的分块自适应图像压缩算法.该文使用KSVD算法对纸箱图像样本集训练出离线字典,采用OMP算法在原始误差下计算图像块初始稀疏系数矩阵,利用改进的Canny边缘检测确定图像块的结构复杂度,分块自适应设定稀疏表示模型二次优化误差,达到对不同图像块针对性的压缩目的.对多幅文字密集程度不同的纸箱图像实验结果表明,该算法在保留图像重要信息的情况下可以达到0.95%～1.95%的压缩率.
Abstract
A block-adaptive image compression algorithm based on dictionary learning is proposed for images with clear structures such as cartons.The KSVD algorithm was used to train an offline dictionary for the carton image sample set,the OMP algorithm was used to calculate the initial sparse coefficient matrix of the image block under the original error,the improved Canny edge detection was used to determine the structure complexity of the image block and adaptively set the sparse representation model's quadratic optimization error for block partitioning,to achieve targeted compression of different image blocks.The experimental results of multiple carton images with different text density show that this algorithm can achieve a compression ratio of 0.95%to 1.95%while preserving the important information of the image.
关键词
字典学习/图像分块/边缘检测/自适应误差/稀疏重构/图像压缩Key words
Dictionary learning/Image block/Edge detection/Adaptive error/Sparse reconstruction/Image compression引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据