QRcode Storage Technology For Image And Text Description

The QRcode plays a fundamental role in the sale of goods in daily life,which can promote the speed of goods trading and further optimize the efficiency.However,QRCode is mainly used for the storage of text,web pages and other information.Although QRCode can store text information,it does not have image information and vivid images,such as images in warehouses and attendance systems.There is also a lack of relevant QRcode research on storage technology for images and text descriptions.Therefore,this paper mainly studies the direct storage of images by QRcode.By using QRCode's high data and high error correction capability,the image description of images and images is directly stored in QRCode without relying on any database,making QRCode like a link.The information that occurs at each stage of the life cycle of the item is linked together to truly improve work efficiency.This paper mainly focuses on two types of images,including the identifying demand-type images and true-color demand-type images,respectively adopting different processing methods,and conducting optimization research according to actual conditions,identifying demand-type images is processing the lines of images.this method sacrifices the true color of the image and is suitable for the processing of simple fingerprints,portraits and other types of images;while the true color demand image is not suitable for sacrificing its true color,using the compression technology that maintains the true color of the image,The image is compression-encoded,and the image is compressed as much as possible to meet the requirements of the two-dimensional code processing capability on the basis of keeping the original information of the image as much as possible.Therefore,this thesis mainly studies and optimizes the algorithm of image extraction and compression coding,and develops the relevant two-dimensional code App according to the actual situation,so that the QRcode image storage is realized on the Android system of mobile phone.According to the actual application of the algorithm in this paper,the specific research and optimization are carried out in the following aspects:(1)For most of the identification demand images involved in this topic,such as portraits,vehicle images,etc.the color of the target and the surrounding area may be relatively complicated,or the texture lines are complex,and the traditional line imageextraction method effect is not ideal enough.To solve the problem of the line image extracted by the traditional method,combined with the application of this paper,the algorithm optimization method of extracting the line image by combining the filter correction and the self-image algorithm is proposed.That is to say,band pass filtering,low-pass filtering and self-image algorithm are combined,and the gray feature is extracted by the method of correcting parameters,and the image is binarized after extracting the appropriate gray feature image.The line image extracted in this way can not only reduce the influence of illumination on the face image,but also adjust the line width according to the requirements of the specific application to adapt to various complicated situations.It can be seen from the final experimental results that the method can obtain a highly effective line image when the compression ratio is satisfied.(2)The compression algorithm used for the true color demand image is optimized.In this paper,the influencing factors of the problem of excessively high complexity of coding unit mode are analyzed.A video coding unit selection algorithm based on deep learning is proposed.Firstly,the algorithm selects the block partition with high coding complexity to study,and optimizes the selection of the super block partition mode.The full-join neural network model in deep learning is applied as the partition model,and the input feature vector is 36.The output is a specific block division mode,and the training mode selects offline training.Secondly,in order to further simplify the model structure and improve the performance of the classifier,the recursive partitioning method of the highly complex quadtree is optimized,and different structures are obtained according to the specific QP value and block size,so as to obtain a four layer and two classification model.Finally,by applying a simplified version of the quadtree to different complex video images,the test results are much less complex than the original quadtree recursive algorithm.The average coding complexity is reduced by 77.84%,and the coding efficiency is very good.A large boost can also improve the compression efficiency of still images.(3)According to the actual situation,the application research is carried out.Firstly,the conversion from the general picture format to the Web P format is completed,and then the true color image of the Web P format is compressed by using the optimized intra prediction algorithm,and the algorithm is packaged and called.To realize the two-dimensional code compression coding of the true color demand image;secondly,through the optimization of the image line extraction algorithm,the optimized algorithm program is packaged and called to realize the two-dimensional code compression coding of the identification demand image;In the Android development environment,the compressed image is converted fromimage to QRcode through a correlation algorithm.Optimize each component,package it into apk,and implement QRcode to store different types of images.Finally,through the development of the QRcode apk,the application of the QRcode image storage on the mobile phone Android system is realized.