面向自动目标识别的图像压缩关键技术研究
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摘要
如何在图像有损压缩过程中保持图像的自动目标识别性能,是军事图像通信领域面临的重要挑战。本文深入研究了面向自动目标识别的图像压缩编码关键技术,取得了一些有价值的研究成果。
图像有损压缩过程中自动目标识别性能保持程度的度量是面向自动目标识别的图像压缩技术的核心问题,本文提出了一种基于轮廓信息保持程度的图像压缩质量测度。在图形间模糊相似性测度的基础上,对原始图像和压缩后恢复图像分别进行相同的轮廓提取处理,将压缩前后轮廓图形间的相似性程度作为轮廓特征保持测度,从而度量了图像有损压缩过程中自动目标识别性能的保持程度。理论分析与实验结果表明,轮廓特征保持测度比二值互相关系数更全面地衡量了图像有损压缩对自动目标识别性能的影响程度。
在分析了ROI检测算法与ROI优先压缩编码技术的基础上,论文分别提出了基于整幅图像ROI检测与基于图像块ROI判决的两种面向自动目标识别的图像压缩系统设计思路和结构框图。在此基础上,论文重点讨论了面向自动目标识别的航空可见光侦察图像压缩编码技术。
团块人造目标ROI检测是面向自动目标识别的航空可见光侦察图像压缩编码系统中的关键环节。本文提出了一种基于局部灰度熵特征的可见光图像团块人造目标ROI检测算法。理论分析和实验结果表明,该算法较好地提取出了人造目标与自然背景的特征差异,能够在各种复杂自然背景和不同目标类型的情况下,有效检测各类团块目标,是一种稳健的团块人造目标ROI检测算法,适用于面向图像压缩的ROI检测任务。
在面向自动目标识别的图像压缩编码系统结构以及可见光图像团块人造目标ROI检测算法的基础上,本文提出了一种基于JPEG2000 ROI优先编码机制的航空可见光侦察图像多级ROI优先压缩编码方案。理论分析与实验结果表明该压缩编码方案能够满足面向自动目标识别的图像压缩任务要求。
It is a great challenge for military image communication technology to preserve the auto target recognition (ATR) performance in the process of lossy image compression. The key techniques in ATR-directed image compression are studied in this dissertation.
How to measure the impact of lossy image compression on ATR performance is a kernel problem in ATR-directed image compression. A novel image compression quality metric based on the contour information is presented in this dissertation. Based on the fuzzy similarity metric between two shapes, the similarity measure between target contour shapes extracted from source image and codec image is defined as contour feature persistence metric (CFPM). Then the extent to which the ATR performance is preserved in lossy image compression is measured. According to theoretical analysis and experiment results, CFPM measures the extent more comprehensively than correlation coefficients.
On the basis of the analysis of region-of-interest (ROI) detection algorithm and ROI prioritized compression mechanism, two ATR-directed compression schemes are presented as: based on full image ROI detection, and based on block ROI judgment. Then the dissertation focuses the issue of ATR-directed aerial reconnaissance optical imagery compression.
Man-made blob ROI detection is an important component in ATR-directed aerial optical image coding. This dissertation presents a novel man-made blob ROI detection algorithm based on local intensity entropy feature. According to theoretical analysis and experiment results, this algorithm extracts the local feature difference between the natural background and man-made objects efficiently, and detects man-made target regions robustly with complicated natural environment and different kinds of targets. It can be treated as a reliable detection algorithm for image-compression-directed ROI detection.
Based on the ATR-directed image compression system structure and the algorithm of man-made blob ROI detection in aerial imagery, an ATR-directed aerial optical image multi-level ROI coding scheme based on JPEG2000 ROI prioritized compression mechanism is presented in this dissertation. According to theoretical analysis and experiment results, the compressing scheme can meet the requirement of ATR-directed image compression.
图像有损压缩过程中自动目标识别性能保持程度的度量是面向自动目标识别的图像压缩技术的核心问题,本文提出了一种基于轮廓信息保持程度的图像压缩质量测度。在图形间模糊相似性测度的基础上,对原始图像和压缩后恢复图像分别进行相同的轮廓提取处理,将压缩前后轮廓图形间的相似性程度作为轮廓特征保持测度,从而度量了图像有损压缩过程中自动目标识别性能的保持程度。理论分析与实验结果表明,轮廓特征保持测度比二值互相关系数更全面地衡量了图像有损压缩对自动目标识别性能的影响程度。
在分析了ROI检测算法与ROI优先压缩编码技术的基础上,论文分别提出了基于整幅图像ROI检测与基于图像块ROI判决的两种面向自动目标识别的图像压缩系统设计思路和结构框图。在此基础上,论文重点讨论了面向自动目标识别的航空可见光侦察图像压缩编码技术。
团块人造目标ROI检测是面向自动目标识别的航空可见光侦察图像压缩编码系统中的关键环节。本文提出了一种基于局部灰度熵特征的可见光图像团块人造目标ROI检测算法。理论分析和实验结果表明,该算法较好地提取出了人造目标与自然背景的特征差异,能够在各种复杂自然背景和不同目标类型的情况下,有效检测各类团块目标,是一种稳健的团块人造目标ROI检测算法,适用于面向图像压缩的ROI检测任务。
在面向自动目标识别的图像压缩编码系统结构以及可见光图像团块人造目标ROI检测算法的基础上,本文提出了一种基于JPEG2000 ROI优先编码机制的航空可见光侦察图像多级ROI优先压缩编码方案。理论分析与实验结果表明该压缩编码方案能够满足面向自动目标识别的图像压缩任务要求。
It is a great challenge for military image communication technology to preserve the auto target recognition (ATR) performance in the process of lossy image compression. The key techniques in ATR-directed image compression are studied in this dissertation.
How to measure the impact of lossy image compression on ATR performance is a kernel problem in ATR-directed image compression. A novel image compression quality metric based on the contour information is presented in this dissertation. Based on the fuzzy similarity metric between two shapes, the similarity measure between target contour shapes extracted from source image and codec image is defined as contour feature persistence metric (CFPM). Then the extent to which the ATR performance is preserved in lossy image compression is measured. According to theoretical analysis and experiment results, CFPM measures the extent more comprehensively than correlation coefficients.
On the basis of the analysis of region-of-interest (ROI) detection algorithm and ROI prioritized compression mechanism, two ATR-directed compression schemes are presented as: based on full image ROI detection, and based on block ROI judgment. Then the dissertation focuses the issue of ATR-directed aerial reconnaissance optical imagery compression.
Man-made blob ROI detection is an important component in ATR-directed aerial optical image coding. This dissertation presents a novel man-made blob ROI detection algorithm based on local intensity entropy feature. According to theoretical analysis and experiment results, this algorithm extracts the local feature difference between the natural background and man-made objects efficiently, and detects man-made target regions robustly with complicated natural environment and different kinds of targets. It can be treated as a reliable detection algorithm for image-compression-directed ROI detection.
Based on the ATR-directed image compression system structure and the algorithm of man-made blob ROI detection in aerial imagery, an ATR-directed aerial optical image multi-level ROI coding scheme based on JPEG2000 ROI prioritized compression mechanism is presented in this dissertation. According to theoretical analysis and experiment results, the compressing scheme can meet the requirement of ATR-directed image compression.
引文
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