A Micro-grain Image Compression Coding Method Based on Content Recognition

Abstract

The invention discloses a micro particle image compression encoding method based on content recognition, which reads the input image data line by line and point by point; detects the current read data to detect whether the target is an object, and the specific checking method is to analyze the current pixel value Whether it is greater than the threshold value, if it is greater than the threshold value, the object is detected and recorded as 1, if it is not greater than the threshold value, the object is not detected and recorded as 0; according to the obtained result, analyze the data on the left and above, Determine whether the current point is the end position of the object. The specific method is, if the left side and the upper left or upper position are all 1, and the bottom of all left pixel points are 0, and the value of the current point is 0, then it is an object the end position of . In the present invention, the image to be encoded is compressed and encoded line by line and point by point, and the cache of image information only caches three lines, and it is no longer necessary to cache the entire image in advance, which not only reduces the occupation of running resource space, but also helps to improve the coding efficiency .

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.