›› 2012, Vol. 32 ›› Issue (4): 54-61.doi: 10.3780/j.issn.1000-758X.2012.04.008

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  1. (北京航空航天大学计算机学院,北京 100191)
  • 收稿日期:2011-11-04 修回日期:2012-04-10 出版日期:2012-08-25 发布日期:2012-08-25
  • 作者简介:杨凯 1981年生,2007年获太原理工大学计算机应用专业硕士学位。现为北京航空航天大学计算机应用专业博士研究生。研究方向为遥感图像处理、压缩及图像质量评价等。
  • 基金资助:


Compensation for Optical Remote Sensing Image Compression Based on Distortion Sensitivity

Yang Kai,Jiang Hongxu   

  1. (School of Computer Science and Technology,Beihang University, Beijing 100191)
  • Received:2011-11-04 Revised:2012-04-10 Online:2012-08-25 Published:2012-08-25

摘要: 可见光遥感图像纹理细节丰富且分布情况复杂,高倍压缩后容易出现失真不均衡现象。现有研究并不针对图像的主观品质且易出现错补偿问题。为此,设计了基于失真敏感性的可见光遥感图像压缩补偿方法。通过对比结构相似度模型各函数对遥感压缩失真的评价效果,构造了压缩失真敏感性模型,在此基础上深入分析了不同程度的数据损失对恢复像质的影响,设计了基于失真敏感性的压缩补偿策略,在压缩编码端确定失真敏感区域并量化回传失真影响明显的数据,补偿于解码端恢复图像中。结果表明,该方法能有效提高恢复图像失真敏感区域内遥感目标的清晰程度和可判读性,降低恢复图像的失真不均衡程度,改善恢复图像的整体质量。

关键词: 图像失真, 数据压缩, 误差补偿, 结构相似度, 可见光遥感

Abstract: High resolution optical remote sensing images are prone to serious local distortion after high compression, whose targets and textures are abundant and complex. Most of the current researches do not focus on subjective image quality, and this will easily lead to over compensation. In order to reduce local distortion, the correlations between SSIM (Structural similarity) component functions and MOS (Mean opinion score) were analyzed on an optical remote sensing compression distortion image database, and a distortion sensitivity model for remote sensing image compression was proposed. Then, this model was utilized to design a compensation approach, and applied to an embedded wavelet image coder. This approach could locate the distortion sensitivity areas and compress the distortion values to reserved space at encoder, and compensate these values into reconstructed image at decoder. Experiment results show that this approach can enhance the visibility and identification of remote sensing objects in the distorted sensitive areas, reduce serious local distortions, and improve the overall image quality.

Key words: Image distortion, Data compression, Error compensation, Structural similarity, Optical remote sensing