Large image real-time browsing method based on multi-core structure

Abstract

The invention discloses a large image real-time browsing method based on a multi-core structure, comprising the following steps: (1) reading image data of appointed positions of a large image into a cache pool by a memory mapping technique; (2) displaying corresponding data blocks inside the cache pool in an image mode according to the appointment of a browser, wherein a displayed image area is acurrent browsing area, and reading and updating image data inside the cache pool when the browser selects an image area adjacent to the current browsing area to browse; and processing a plurality of threads by setting parallel working pipelines and utilizing a CPU multi-core technique when the image data of the current browsing area are displayed and the image data of the cache pool are read in the step (1) and the step (2). The large image real-time browsing method greatly enhances the browsing efficiency without any preprocessing operation by using the parallel working pipelines and being based on a predicted data processing technique.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for real-time browsing of large images based on a multi-core architecture. Background technique [0002] In recent years, with the continuous improvement of data acquisition equipment, more and more high-precision large images have appeared in the fields of medicine, remote sensing, meteorology, and archaeology. While these large images bring more detailed information to the user, it also brings great challenges to the computer to display them interactively. For example, the data size of the panorama of the "Flying Apsaras" Buddha statue we shot in Cave 285 of Mogao Grottoes in Dunhuang with a Canon 5D camera reached 15G, which far exceeded the capacity limit of computer memory and video memory, resulting in inefficient out-of-core Data access has brought huge problems to the real-time browsing of pictures. [0003] Previous research on large image browsing mo...

AI Technical Summary

Problems solved by technology

There are two obvious deficiencies in these algorithms: First, these algorithms require a large number of preprocessing operations, which are very time-consuming and greatly reduce the operating efficiency; second, these algorithms are designed by designing some storage hierarchies. Cache the accessed data, using the time correlation and space correlation of data usage. However, once new image data needs to be accessed, it has to be dispatched from the hard disk, resulting in obvious I / O delay, which makes the real-time browsing performance of images suddenly decline

In order to fully tap the parallel computing power of the hardware to realize real-time browsing of large images, the data structure and algorithm must be redesigned, otherwise the processing power of the existing hardware cannot be utilized

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