Compression Method of Satellite Cloud Image

Abstract

The invention provides a satellite cloud picture compression method. The satellite cloud picture compression method comprises the steps: firstly, acquiring at least one cloud system image from each first satellite cloud picture according to characteristic information of pixel points contained in a plurality of stored first satellite cloud pictures; combining the pixel points with characteristic information difference smaller than a first threshold value into a pixel set based on comparison of the characteristic information of continuous adjacent pixel points contained in the cloud system images, and simplifying the cloud system image so as to obtain a cloud system template; then matching each cloud system template with the acquired second satellite cloud picture so as to select the cloud system template with highest matching degree; resolving the second satellite cloud picture into the selected cloud system template and a non-matched second satellite cloud picture; and repeating the final step until the characteristic information of pixel points contained in the rest of non-matched satellite cloud picture is lower than a second threshold value, thus obtaining a plurality of cloud system template sets and the rest of non-matched second satellite cloud pictures.

Description

technical field [0001] The invention relates to an image compression method, in particular to a satellite cloud image compression method. Background technique [0002] In the large amount of data contained in the satellite cloud image, the cloud system data accounts for the main component. Therefore, when compressing the satellite cloud image, the compression effect and compression loss of the cloud system data need to be specially considered. [0003] However, the current image compression algorithms for satellite cloud images still use existing image compression algorithms, such as compressing satellite cloud images as general black-and-white images or binary data, and taking the visual quality of the image or the signal-to-noise ratio of the restored data as the Evaluation criteria for lossy compression. This method cannot reflect the correlation and similarity between satellite cloud images and the priori nature of cloud system data, so the compression effect is not goo...

AI Technical Summary

Problems solved by technology

This method cannot reflect the correlation and similarity between satellite cloud images and the priori nature of cloud system data, so the compression effect is not good and the compression efficiency is low

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