WAVELET IMAGE COMPRESSION BY USING HYBRID KERNEL SVM

摘要：

In this paper, we proposed a way through combining the support vector machines (SVM) with hybrid kernel and wavelet transform to compress the image. SVM regression could learn dependency from training data and realized compression by using fewer training point (support vectors) to represent the original data and eliminate the redundancy. Wavelet coefficients could be compressed based on this feature. Further more, the hybrid kernel applied can enhance the compress efficient and improve the picture quality by controlling the VC- dimension of SVM. At last, we use the arithmetic coding to encode the dates from the output of the SVM and finish the image compression.

关键词： Image Compression Support Vector Machines Hybrid Kernel Wavelet Transform VC-dimension

作者: JIA-MING CHEN LEI LI LING-YE NIE

作者单位: Automation Institution, Nanjing University of Posts and Telecomunications, Nanjing 210003, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3056-3060

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）