CAJ | 학술논문

针对自然图像压缩收敛速度慢的问题，提出一种新的基于峭度的绝对值和固定系数方差的稀疏编码SC（Sparse Coding）算法。该算法采用稀疏性惩罚函数来表示峭度大小，同时保证了图像特征系数的分散性与独立性，并维持图像重构误差和稀疏惩罚函数之间的平衡，能够更有效地提取图像的边缘特征和局部特征。通过选取合适的特征基函数，有利于加快所提出的SC网络的收敛速度。应用该算法可以成功地提取自然图像的特征基向量，进一步利用特征系数的稀疏性，有效实现自然图像的压缩。仿真实验结果表明，与基于标准独立分量分析（ICA）和离散余弦变换（DCT）的图像压缩方法相比，基于峭度准则的稀疏编码图像压缩方法具有较快的收敛速度及较好的有效性和实用性。
침대자연도상압축수렴속도만적문제，제출일충신적기우초도적절대치화고정계수방차적희소편마SC（Sparse Coding）산법。해산법채용희소성징벌함수래표시초도대소，동시보증료도상특정계수적분산성여독립성，병유지도상중구오차화희소징벌함수지간적평형，능구경유효지제취도상적변연특정화국부특정。통과선취합괄적특정기함수，유리우가쾌소제출적SC망락적수렴속도。응용해산법가이성공지제취자연도상적특정기향량，진일보이용특정계수적희소성，유효실현자연도상적압축。방진실험결과표명，여기우표준독립분량분석（ICA）화리산여현변환（DCT）적도상압축방법상비，기우초도준칙적희소편마도상압축방법구유교쾌적수렴속도급교호적유효성화실용성。
As the natural image compression has the problem of slow convergence rate,we put forward a new sparse coding (SC ) algorithm which is based on the absolute value of kurtosis and fixed parameter variance.This algorithm adopts sparse penalty function to indicate the kurtosis size,and meanwhile ensures the dispersibility and independence of the image feature coefficients,as well as maintains the balance between image reconstruction error and sparse penalty function,thus is able to extract the edge features and local features of the image more efficiently.By selecting proper feature base function,it is conducive to expedite the convergence speed of the proposed SC network.To apply this algorithm can successfully extract the feature base vector of nature image,as well as further utilises the sparseness of the feature coefficient and effectively achieves the compression of nature image.Simulation experiment results showed that,compared with the image compression methods based on standard isolated component analysis (ICA)and discrete cosine transform (DCT),the sparse coding image compression method based on kurtosis criterion had faster convergence rate and better effectiveness and practicability.