Offline image optimization method and system

Abstract

The invention provides an offline image optimization method and system. The offline image optimization method comprises the following steps: an offline user sends out a request of visiting a first page; the request is positioned into a network server storing the first page; the network server monitors the request and judges whether a first image exists in the first page; if yes, the first image is compressed so as to generate a second image; and the second image is used for replacing the first image in the network server. According to the method of the embodiment of the invention, on the one hand, the offline optimization can be carried out by fully using offline resources so that the reduction of the performance of an online server caused by the image optimization and the security risk are avoided, a developer does not need to manually optimize the image, thereby the development efficiency is improved; and on the other hand, the image is compressed and optimized so that the size of a paging file can be reduced, the flow expenditure is reduced, the cost of network bandwidth is saved, the transmission time of the image is shortened, the waiting time of the user is further reduced, and the user experience is promoted.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an offline image optimization method and system. Background technique [0002] With the arrival and popularization of web2.0, network applications become more and more client-rich, and the rich client technology with strong interactivity brings users a higher and more comprehensive network experience. What has brought about is that the proportion of pictures in web network applications is increasing. How to reduce the image size as much as possible has become an important breakthrough in the performance optimization of web applications. [0003] In the prior art, picture optimization is mainly done manually by the front-end development engineer. For example, the development engineer uses a separate picture optimization tool. Every time the picture is optimized, the development engineer needs to manually optimize the picture and re-save the picture path. [0004...

AI Technical Summary

Problems solved by technology

[0004] The problem existing in the existing technology is that, on the one hand, the lossless compression rate of pictures varies with the knowledge and experience of the development engineers themselves, and it is impossible to reduce the picture size efficiently and sufficiently; on the other hand, the development engineers use independent optimization tools, and the operation is relatively It is cumbersome, and needs to be optimized every time an image is updated or added, which affects development efficiency. Sometimes development engineers even ignore or give up image optimization because of the unfriendly image optimization methods used by independent tools

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