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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Visual Distortion Sensitivity Modeling for Spatially Adaptive Quantization in Remote Sensing Image Compression
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Visual Distortion Sensitivity Modeling for Spatially Adaptive Quantization in Remote Sensing Image Compression

机译：遥感图像压缩中空间自适应量化的视觉失真敏感度建模

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摘要

As remote sensing images are often characterized with strong randomness, weak local correlation, and multiple small targets, the commonly used coarse-granularity subband-level quantization scheme fails to make use of these characteristics; thus, the performance improvements of these methods in literature are often marginal. To address this problem, this letter presents a novel spatially adaptive quantization (SAQ) method for the compression of remote sensing images based on our proposed Visual Distortion Sensitivity (ViDiS) Model. The ViDiS model takes into consideration four ViDiS components, including image luminance, spatial frequency, spatial orientation, and visual masking, to help measure the distortion more consistent to the image quality perceived by human beings. Then, a SAQ scheme is proposed to better exploit the content characteristics of remote sensing images, in which the quantization is conducted on a finer subband block level rather than subband level, with the guidance of the ViDiS model. Experimental results show that the proposed algorithm can preserve better visual quality in low-contrast areas with small targets at a competitive computational cost, which makes it more desirable in compression applications for remote sensing images.
机译：由于遥感图像通常具有较强的随机性，较弱的局部相关性和多个小目标的特征，因此常用的粗粒度子带级量化方案无法利用这些特征。因此，这些方法在文献中的性能改进通常是微不足道的。为了解决这个问题，这封信提出了一种新颖的空间自适应量化（SAQ）方法，该方法基于我们提出的视觉失真敏感度（ViDiS）模型来压缩遥感图像。 ViDiS模型考虑了四个ViDiS组件，包括图像亮度，空间频率，空间方向和视觉遮罩，以帮助测量更符合人类感知图像质量的失真。然后，提出了一种SAQ方案，以更好地利用遥感图像的内容特征，其中在ViDiS模型的指导下，在更精细的子带块级别而不是子带级别上进行量化。实验结果表明，该算法可以在具有较小目标的低对比度区域保持较好的视觉质量，而其计算成本却具有竞争力，这使其在遥感图像的压缩应用中更为理想。

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