Micro-particle image compression coding method based on content recognition

Abstract

The invention discloses a micro-particle image compression encoding method based on content recognition. The method comprises the following steps: reading input image data line by line and point by point; detecting the current read-in data, detecting whether a target is an object or not, wherein the specific detection method comprises the steps that whether a current pixel value is larger than a threshold value or not is analyzed, if yes, the object is detected and marked as 1, and if not, the object cannot be detected and marked as 0; according to the obtained result, analyzing the data of the left side and the upper side, judging whether the current point is the ending position of the object or not, wherein the specific method comprises the steps that if the positions of the left side, the upper left side or the upper left side are all 1, the lower portions of all left side pixel points are 0, and the value of the current point is 0, the current point is the ending position of the object. According to the invention, the image to be coded is compressed and coded line by line and point by point, the cache of the image information only caches three lines, and the cache of the imageinformation does not need to cache the whole image in advance, so that the occupation of an operation resource space is reduced, and the coding efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and specifically relates to a method for compressing and encoding tiny particle images based on content recognition. Background technique [0002] Image processing technology is an interdisciplinary field. With the continuous development of computer science and technology, image processing and analysis has gradually formed its own scientific system, and new processing methods emerge in endlessly. Although its development history is not long, it has attracted people from all walks of life. widespread attention. First of all, vision is the most important means of human perception, and images are the basis of vision. Therefore, digital images have become an effective tool for scholars in many fields such as psychology, physiology, and computer science to study visual perception. Second, image processing has a growing demand in large-scale applications such as military, remote sensing, and ...

AI Technical Summary

Problems solved by technology

[0003] Image compression coding can be divided into two categories: one type of compression is reversible, that is, the original image can be completely restored from the compressed data, and there is no loss of information, which is called lossless compression coding; the other type of compression is irreversible, that is, from the compressed The final data cannot completely restore the original image, and there is a certain loss of information, which is called lossy compression coding. There is no special compression coding method for tiny granular images, and general digital image compression coding is used. This compression coding method is mainly The computational complexity of the algorithm is high, and it does not take advantage of the characteristics of micro-grained images, so it cannot be well compressed and encoded. Therefore, we propose a method for compressing and encoding micro-grained images based on content recognition.