Multimedia Image Research Articles

Retracted: Research on High Robust Multimedia Image Encryption Based on Gyrator Transform Domain Model

Retracted: Research on High Robust Multimedia Image Encryption Based on Gyrator Transform Domain Model

Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology

The rapid development of the information age brings convenience to human life, but it also brings great challenges to information processing technology. Multimedia enhancement technology is an organic combination of multimedia technology and information processing technology, and it is also an important way of modern multimedia image information processing. However, its usefulness and effectiveness are increasingly negatively affected by the open information environment. The processing effect is also unable to meet the development requirements of the visual field. In order to improve this problem, this paper studied the image transmission and processing system of network-controlled robot on the basis of analyzing the characteristics of the problems existing in the current stage of multimedia technology. On this basis, a new image processing variational model was established and applied to multimedia enhancement technology, which improved the efficiency and effect of image information processing. Finally, the feasibility of its application function and performance was tested by experiments. The test results showed that in the difficult mode of the image processing task, the refresh time of the model in this paper in the multimedia enhancement technology was 1.13 s in total, which was not much different from the test results in the easy mode. Also, in the load stress test, the comprehensive test values under full-function operation and partial-function operation were 42.04% and 20.92%, respectively. Compared with the traditional model, the model in this paper has stronger carrying capacity in multimedia enhancement technology and has better processing ability and stability.

Retraction Note: A novel large-scale multimedia image data classification algorithm based on mapping assisted deep neural network

Retraction Note: A novel large-scale multimedia image data classification algorithm based on mapping assisted deep neural network

Retraction Note: Infrared pedestrian detection algorithm based on multimedia image recombination and matrix restoration

Retraction Note: Infrared pedestrian detection algorithm based on multimedia image recombination and matrix restoration

Retraction Note: Evaluation and simulation of medical sports health equipment multimedia image based on information asymmetry theory

Retraction Note: Evaluation and simulation of medical sports health equipment multimedia image based on information asymmetry theory

Retraction Note: Test and simulation of health balance training equipment based on medical multimedia image for exercise balance ability

Retraction Note: Test and simulation of health balance training equipment based on medical multimedia image for exercise balance ability

Image Retrieval Using Digital Image Inpainting Techniques

Image retrieval is an inverse problem in digital image processing. In this paper, the authors deal with restoration of image using digitally image inpainting methods. In this inpainting technique, one can extract a missing an important part or can fill unwanted parts of an image, so as to recover original information without loss. They are using semi-automatic and fast image inpainting methods and compared their result in terms of image quality assessment parameters such as PSNR, MSE, as well as required timing constraints. The observational as well as statistical results show that the fast image inpainting technique is superior in all respects for any kinds of multimedia image application.

Multimedia Image Data Compression Based on Wavelet Analysis

In order to improve the compression technology of multimedia image data, a compression method of multimedia image data based on wavelet analysis was proposed. By combining Fourier transform and wavelet packet multithreshold denoising method, the image data was denoised and compressed through the transform of wavelet analysis and multithreshold processing, so as to realize the compression of image data with high quality. The experimental results showed that it was effective to analyze signal singularity, singularity position, and singularity degree by using wavelet analysis. And the signal mutation position near t = 0 could be seen clearly from the wavelet coefficient diagram, while the Fourier transform diagram did not provide any information about when the frequency mutation occurred. It was concluded that the method of multimedia image data compression based on wavelet analysis could promote the development of multimedia image data compression technology effectively.

Simulation of Multimedia Visual Image Motion Track Marking Based on Artificial Bee Colony Algorithm

With the rapid development of electronic technology, network technology, and multimedia processing technology, people pay more and more attention to the surrounding environment. Paying attention to and handling emergencies have also become one of the main points of attention. Through the combination of multimedia and monitoring system, the collected image data are processed and integratedly controlled by the computer platform, and an intelligent monitoring system for traffic road safety can be obtained. Due to the advantages of multimedia visual image motion trajectory identification technology in road traffic safety, it can effectively prevent the chaos and influence caused by humans in chaotic situations, making it widely used. By identifying the motion trajectories of different objects, road emergencies can be effectively prevented and countermeasures can be established. Based on this, this study proposes the artificial bee colony (ABC) algorithm to identify the motion trajectory in the visual image, but the standard ABC algorithm is too slow to converge in the later stage of trajectory recognition. Therefore, this study is based on the standard ABC algorithm. The trajectory recognition simulation shows that the optimized algorithm accelerates the convergence speed and the ability of the global optimal solution in the trajectory recognition process. Then, the optimized ABC algorithm is used to identify the multimedia visual image trajectory. The experiment shows that the average correct rate of feature extraction is also about 98%. The data identification results also show that the optimized ABC algorithm fits the real trajectory better in the trajectory identification. The running time of the algorithm is shorter than the running time of other comparison algorithms, which fully shows the accuracy and superiority of the algorithm in this study.

(Retracted) Computer hand-painting of intelligent multimedia images in interior design major

With the advent of the information age, the rapid development of computer software and hardware and related high-tech equipment has made today’s interior design expression and display forms increasingly diversified, providing designers with a broad performance platform. On the basis of clarifying the ideas of the combined application of hand-drawn and computer graphics, we analyze the methods and approach of their combined application and expound on the actual application. It is hoped that the research can provide design ideas and references for the interior design industry and teaching activities. The Small Business Innovation Research (SBIR) program consists of three phases, the first of which is a feasibility study for the innovation. If the innovation is feasible, enter the second stage to finalize the innovative product, and after private and public sector investment, the third phase, commodity promotion and commercialization. The influence of intelligent equipment such as computers and hand-painted boards on the design industry is obvious. With the development of the social economy and the era of technological innovation, a social culture and a value system will be formed. Therefore, the arrival of media technology will also generate perceptions, ways of thinking, and value orientations and will affect our social interaction space, people’s main living places, and indoor space. The software module in the computer provides designers with the opportunity to use computer design software to quickly present the designer’s ideas on an electronic display screen. Its increasingly convenient design conditions promote the diversity and richness of design software. Combining intelligent multimedia images, with the help of compressed image noise reduction programming, SBIR technology, etc., it has created the development of an interior design. The number of students studying interior design has increased by nearly 60% every year, and the popularity of smart multimedia technology has also reached 78.9%. In the media age, people and interior designers are constantly changing their aesthetic tastes, narrative methods, expression methods, life function needs, and aspirations for a spiritual destination. Good interior design methods do not necessarily produce excellent interior design works, but good interior design works must be guaranteed by a set of practical design techniques and procedures. The article attempts to tap the multiple values of multimedia in indoor space to build a more humanized, humane, and media-inspired indoor space.

User data privacy in multimedia domain using 3–3 LSB-based color image steganography with RC4 and Bernoulli map protection

Social media sites such as Facebook, Twitter, etc. have become commonplace in everyday life. With the emergence of the internet, the amount of data being created and communicated within social networks has increased exponentially. As much as we enjoy the exchange of information that occurs on social media, protecting and maintaining privacy demands a lot of effort. Users’ data that are to be kept private should be treated as confidential information. As a result, securing data exchange on the internet has become a critical and urgent issue. We have suggested a method for protecting user data which is encrypted by using the Rivest Cipher 4 (RC4) algorithm in multimedia images with the help of 3–3 least significant bits (LSB) techniques, with the added safety of Bernoulli map. We call this technique the “user data protection scheme.” 3–3 LSB is a message hiding process in which the first three stand for three LSB and the next three stand for all three channels (R, G, and B). In this method, we are using the Rivest Cipher 4 (RC4) encryption algorithm to encrypt the user’s data, which will then be embedded into the multimedia image in the next step. With the help of the Bernoulli map, we generate a random sequence of length similar to the size of the image. For every random number generated by the Bernoulli map, we determine the location in the plane and place three bits of secret message into the cover image using the LSB technique. Since we have used two different encryption techniques, i.e., RC4 for encryption of user data and Bernoulli map for embedding encrypted data at random locations over the cover image, our method is more secure than others. We performed this analysis to equate peak signal-to-noise ratio (PSNR) and mean square error. Our experimental result shows that 9 bpp can be embedded in color images with a PSNR higher than 42 dB, and for the gray scale image embedding capacity is 3 bpp with a PSNR higher than 42 dB, which provides better results. Apart from this, it produces less distorted stego-images than currently used methods (an average of 1.68 bpp).

Multispectral Remote Sensing Data Analysis Based on KNNLC Algorithm and Multimedia Image

In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. It is found that the KNN algorithm is one of the classic algorithms of data mining. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. However, the algorithm inevitably has many problems, such as how to determine the appropriate K value, the unsatisfactory effect of data processing for some special distributions, and the unacceptable computational complexity of high-dimensional data. In order to solve these shortcomings, the researchers proposed the KNNLC algorithm. Then, taking the classification experiment as an example, through the comparison of the experimental results on different data sets, it is proved that the average level of the classification performance of the KNNLC algorithm is better than the classic KNN classification algorithm. The KNNLC algorithm shows better performance in most cases, with an accuracy rate of 2 to 5 percentage points higher. An improved algorithm is proposed for the nearest neighbor selection strategy of the traditional KNN algorithm. First, in theory, combined with the theory of sparse coding and locally constrained linear coding, the classical KNN algorithm is improved, and the KNNLC algorithm is proposed. The comparison of the experimental results on the data set proves that the average level of the KNNLC algorithm is better than the classical KNN classification algorithm in terms of classification performance.

The Paybacks of Using Online Multimedia in Training for Resident Doctors

This paper analyses the use of multimedia technology in training orthopaedic residents and assesses how such aids can improve residents’ ability to master treatments in orthopaedic surgery. To this end, a total of 120 resident trainees who had recently graduated from clinical medicine were recruited for this study, and were classified into two groups. Based on which, the new teaching model using multimedia aids was studied and compared with traditional teaching model (mainly with oral presentation). . The results show that resident trainees with the new teaching model could be more active in thinking ability and interest, and have improved learning outcomes, compared with those with traditional teaching models. Meanwhile, the former trainees showed reduced passive, thereby their understating of key points was strengthened. Overall, the use of multimedia images improved the quality of teaching, transformed the teacher-centred clinical teaching model, and increased trainees’ level of satisfaction with the teaching methods. Therefore, this paper concludes that the use of multimedia images can significantly improve the quality of clinical teaching for resident trainees and should be widely promoted.

Multimedia Image in Physical Education

With the progress of society and the rapid development of science and technology in the world, the 21st century will be the multimedia era. Therefore, physical education teaching in colleges and universities should take the initiative to seize the opportunity, adapt to social needs, change traditional teaching concepts, introduce modern teaching methods into physical education teaching, enrich physical education teaching content, stimulate students’ interest in sports knowledge and sports technology learning, broaden knowledge horizons, improve teaching quality, and promote the development of physical education. The traditional teaching mode restricts the development of physical education (PE) in many aspects, and one of the important contents of modern teaching methods is to master and use modern teaching equipment and means. Therefore, it is necessary to timely change the educational thought, update the educational concept, and actively explore and research new teaching methods that are suitable for the educational concept of the times, to ensure the adaptability, scientificity, and creativity of teaching methods. Based on the above background, the research content of this paper is the application of multimedia CAI technology in PE teaching. Using the methods of document interviews, analysis and induction, and online retrieval, on the basis of reading a large number of professional theoretical books on physical education, multimedia technology, and modern teaching and reform, the application of multimedia technology in physical education teaching in colleges and universities is analyzed. The application is researched at the theoretical level. This paper takes PE college 2019 students as the simulation object. The results show that the pass rate of professional English after the experiment is 15.33% higher than that before the experiment.

Automatic panoramic medical image stitching improvement based on feature-based approach

Clarification publications in the medical field are very important in making doctors the right decision by finding evidence to support his decision, therefore, the importance of collecting medical images and combining them with multiple overlapping areas of the same scene is important. This (processing, multimedia images and their medical applications) is very difficult. Our system proposed in this paper is applicable to the medical field of scoliosis and other Rib cage. The problem is the narrow vision of the X-ray machine and the lack of a large picture in one frame, the best solution is to combine more than one x-ray image into one panoramic image, our proposed method relies on in light of feature based methodology by Circle (Oriented-FAST and Rotated-BRIEF). The rapid wave approach is used to describe the feature through the use of BRIEF technology, the standard that has been adopted in our technology to describe the performance of the planning is based on the processing time and image quality created. The purpose of using the feature extraction approach in our technology is to obtain a high-resolution panoramic image plus short processing time, the results that we were able to obtain, according to the experimental results applied, resulted in ORB image quality and recording time.

Biological Breakdown of Sports in Athletics Based on Multimedia Image Acquisition Techniques.

This paper provides an in-depth analysis and study of the decomposition of sports biology of athletics events through multimedia image acquisition techniques. The proposed design uses SOPC hardware-software collaboration technology, which makes full use of the parallelism of hardware as well as the flexibility of software to essentially improve the speed of image processing and greatly improve the efficiency of image processing. The high speed and parallelism of hardware are used to realize the image acquisition part with high timing requirements and the preprocessing algorithm with a large number of operations and strong repetition, in addition to the difficulties of many initialization configuration parameters of each peripheral. Additionally, the need for frequent adjustment of peripheral working parameters is solved by making full use of the scalability and flexibility of software, and the control signals output by software can coordinate the work of each hardware module to ensure that each module of the system cooperates. It is also possible to coordinate the work of each hardware module through the control signal output from the software to ensure that each module of the system cooperates and operates in an orderly and efficient manner and provides the possibility of realizing a higher level of the image processing algorithm. The system includes four main factors: swinging action, supporting action, vacating action, and speed rhythm. Based on this system, 14 key kinematic indicators were selected to reflect the technical status of the youth walkers. The landing angle decreased with the increase of speed, and the landing technique was insufficient; it had better control of the large and small arm angle, and their stride width was open, while the other four athletes showed increased tension in the hip joint and upper limb with the increase of walking speed, as well as the wrong action of forward and internal rotation of the large and small arms.

Multi-modal active learning with deep reinforcement learning for target feature extraction in multi-media image processing applications

Multi-modal active learning with deep reinforcement learning for target feature extraction in multi-media image processing applications

Deep learning-based automatic annotation and online classification of remote multimedia images

Deep learning-based automatic annotation and online classification of remote multimedia images

FastSS: Fast and smooth segmentation of JPEG compressed printed text documents using DC and AC signal analysis.

With the surge of COVID-19 pandemic, the world is moving towards digitization and automation more than it was presumed. The Internet is becoming one of the popular mediums for communication, and multimedia (image, audio, and video) combined with data compression techniques play a pivotal role in handling a huge volume of data that is being generated on a daily basis. Developing novel algorithms for automatic analysis of compressed data without decompression is the need of the present hour. JPEG is a popular compression algorithm supported in the digital electronics world that achieves compression by dividing the whole image into non-overlapping blocks of 8 × 8 pixels, and subsequently transforming each block using Discrete Cosine Transform (DCT). This research paper proposes to carry out Fast and Smooth Segmentation (FastSS) directly in JPEG compressed printed text document images at text-line and word-level using DC and AC signals. From each 8 × 8 block, DC and AC signals are analyzed for accomplishing Fast and Smooth segmentation, and subsequently, two Faster segmentation (MFastSS) algorithms are also devised using low resolution-images generated by mapping the DC signal (DC Reduced Image) and encoded DCT (ECM Image) coefficients separately. Proposed models are tested on various JPEG compressed printed text document images created with varied space and fonts. The experimental results have demonstrated that the direct analysis of compressed streams is computationally efficient, and has achieved speed gain more than 90% when compared to uncompressed domains.

A Deep Learning and Machine Learning Approach for Image Classification of Tempered Images in Digital Forensic Analysis

Multimedia images are the primary source of communication across social media and other websites. Multimedia security has gained the attention of modern researchers and has posed dynamic challenges such as image forensics, image tampering, and deep fakes. Malicious users tamper with the image embedding noise, leading to misinterpretation of the content. Identifying and authenticating the image by detecting the forgery operations performed on it is essential. In our proposed model, we detect the forged region using the machine learning model SVM in the first iteration and Convolution Neural Network in the second iteration with Discrete Cosine Transform (DCT) for feature extraction. The proposed model is tested with a Corel 10K dataset, and an average accuracy of 98% is obtained for all kinds of image operations, including scaling, rotation, and augmentation.