The invention discloses an
image compression method based on adaptive
quadtree segmentation and standard deviation optimization, and provides an efficient compression scheme driven by dynamic statistics aiming at the problems of over-segmentation, low calculation efficiency and detail loss under a high compression rate in a traditional
quadtree compression
algorithm. Specifically, the
compression ratio and the reconstruction quality are effectively balanced by calculating the RGB channel standard deviation of an image region in real time and combining region area weighted income analysis to adaptively control the recursive segmentation depth; an area weighted error constraint mechanism is introduced, segmentation of a large-size low-variance region is terminated preferentially, and the redundant data volume is remarkably reduced; a standard deviation-driven priority
ranking strategy is adopted, fine
processing is performed on high-complexity sub-regions, and rapid approximation is realized by combining mean color filling, so that the
algorithm efficiency is optimized while the peak
signal-to-
noise ratio is ensured. Through the synergistic effect of the technologies, on the premise that high-frequency details of the image are reserved, calculation complexity and storage requirements are greatly reduced, the method is suitable for real-time image transmission, mobile terminal
processing and low-bandwidth communication scenes, and a reliable solution is provided for high-precision and low-
delay compression requirements.