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Spatial Complexity Reduction in Remote Sensing Image Compression by Atomic Functions
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DOI：10.1109/LGRS.2022.3213406.png)
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Abstract
En 中文
Remote sensing (RS) digital images have a great variety of applications in solving real-world problems. Modern sensors provide this type of data of a very high resolution, which, in combination with a great number of acquired images, makes a problem of compressing RS images of particular importance. In this letter, discrete atomic compression (DAC) and a problem of its spatial complexity reduction are considered. This approach provides data compression and protection features in combination with such image representation that is ready for applying different artificial intelligence methods. For this reason, its application to image processing is relevant. Several modifications that provide reducing the spatial complexity of DAC are proposed, and their efficiency is analyzed. In particular, it is shown that using a block splitting procedure, it is possible to get a significant decrease in additional memory expenses without DAC's efficiency degradation in terms of lossy image compression.
Keywords:
Image coding
Complexity theory
Transforms
Memory management
Discrete cosine transforms
Transform coding
Digital images
Atomic functions
discrete atomic compression (DAC)
discrete atomic transform (DAT)
lossy image compression
spatial complexity
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