CAJ | 학술논문

根据超光谱图像空间谱间都存在较强相关性的特性,设计了一种结合空间预测的二阶差分预测压缩算法.采用MED预测器去除空间相关,采用二阶差分预测器去除谱阃相关,并根据像素预测误差的权重设计了统一的去相关预测器,最后对误差图像做基于上下文的编码,实现图像的近无损压缩.研究结果表明,各波段峰值信噪比(PSNR)为39dB左右时,压缩比可以达到12.7,压缩效果比较理想.
근거초광보도상공간보간도존재교강상관성적특성,설계료일충결합공간예측적이계차분예측압축산법.채용MED예측기거제공간상관,채용이계차분예측기거제보곤상관,병근거상소예측오차적권중설계료통일적거상관예측기,최후대오차도상주기우상하문적편마,실현도상적근무손압축.연구결과표명,각파단봉치신조비(PSNR)위39dB좌우시,압축비가이체도12.7,압축효과비교이상.
According to hyper-spectral images having strong correlation both in spectral and spatial,a novel compression scheme based on second order difference predictive that combines with spatial predictive was presented.MED predictor was used to remove the spatial correlation.Second order difference predictor was used to remove spectral correlation.Then a unified predictor was designed based on the weight of predictive error.At last near lossless compression was completed after context-based coding.The results show that the compression ratio can reach up to 12.7 when the PSNR is about 39dB,so the algorithm is efficient.