Abstract
In order to improve the effect of multimedia data transmission, this paper integrates scene elements to analyze the needs of multiple types of data transmission and uses the technology of the Internet of Things to analyze the compression and transmission of multimedia data communication. Moreover, this paper proposes a reversible information hiding algorithm for encrypted images based on pixel sorting and grouping prediction and discusses how to improve the prediction accuracy through the histogram of prediction errors. In addition, this paper designs a communication compression transmission system in a multimedia and Internet of Things environment integrating scene elements and builds system function modules and system processes. Finally, this paper verifies the performance of the proposed system by means of simulation experiments. From the experimental results, we can see that the system proposed in this paper has a good multimedia information transmission effect.
Highlights
With the rapid development of multimedia technology and Internet technology, the relationship between the two is getting closer and closer
Any terminal on the network can share multimedia information and can store, process, and retransmit the acquired multimedia data. at is to say, network multimedia is developed on the basis of computer, multimedia, and network
(2) Compared with traditional multimedia, the computer equipment used in network multimedia is quite special. e way that network multimedia processes information such as images and sounds is different from that of general multimedia, so the requirements for hardware equipment are higher [3]. e quality of the image is determined by the number of frames transmitted per second and the size of the image
Summary
With the rapid development of multimedia technology and Internet technology, the relationship between the two is getting closer and closer. After analyzing the joint sparse model and the spatial correlation between nodes in the wireless sensor network, the DCS encoding and decoding algorithm was adopted, and the energy of the node was used as an evaluation index to perform a series of operations of distributed compressed sensing. According to the encryption key, a random matrix R with the same size as the image is generated, the image I and the random matrix R are added, and modulo 256 is added to obtain the encrypted image E. e specific encryption process is shown in the following formula [22]: E(i, j) (I(i, j) + R(i, j))mod256. When the image is decrypted, the encrypted image E is subtracted from the random matrix R and modulo 256 to obtain the original image I. e specific calculation process is as shown in the following formula: I(i, j) (E(i, j) − R(i, j))mod256. In the Arnold inverse transformation, we only need to change the matrix to the inverse matrix of the original elementary matrix. e specific calculation is as follows:
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