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Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology Application of Image Processing Variation Model Based on Network Control Robot Image Transmission...
Wu, Yanmin;Qi, Jinli
2022-09-29 00:00:00
Hindawi Journal of Robotics Volume 2022, Article ID 6991983, 10 pages https://doi.org/10.1155/2022/6991983 Research Article Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology 1 2 Yanmin Wu and Jinli Qi Department of Arti
cial Intelligence and Big Data, Chongqing College of Electronic Engineering, Chongqing 401331, China Department of General Education and International Studies, Chongqing College of Electronic Engineering, Chongqing 401331, China Correspondence should be addressed to Yanmin Wu; wuyanmin@cqcet.edu.cn Received 20 July 2022; Revised 30 August 2022; Accepted 17 September 2022; Published 29 September 2022 Academic Editor: Shahid Hussain Copyright © 2022 Yanmin Wu and Jinli Qi. �is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. �e 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 infor- mation processing technology, and it is also an important way of modern multimedia image information processing. However, its usefulness and e†ectiveness are increasingly negatively a†ected by the open information environment. �e processing e†ect is also unable to meet the development requirements of the visual ‡eld. 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 eŒciency and e†ect of image information processing. Finally, the feasibility of its application function and performance was tested by experiments. �e test results showed that in the diŒcult 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 di†erent 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. brought great challenges to the current multimedia en- 1. Introduction hancement technology. When there is a lot of complicated With the maturity of Internet technology and the wide- and dazzling multimedia image information, how to com- press it to reduce the transmission amount and improve the spread use of mobile communications, the image infor- mation used and circulated in the online environment has transmission and processing eŒciency on the basis of shown a rapid development trend. As an important support retaining the e†ective information is a problem that needs to for image information processing, multimedia enhancement be solved by the current multimedia enhancement tech- technology can e†ectively process various types of multi- nology. In the context of the dual development of control media information, such as text, data, video, and voice. technology and computer network, network-controlled However, in the whole complex communication network robots have been maturely applied. Also, its image trans- environment, image information has strong redundancy. All mission and processing system ensures the convenience and kinds of image content, whether in the process of storage, reliability of image processing. �e image processing vari- transmission, or in the process of secondary analysis, have ational model established under this system can ensure that 2 Journal of Robotics estimation were unified into a variational optimization the key information of the image is not lost. *e reliability and authenticity of the image are improved, which is of great problem [12]. *e image processing variational model of the network-controlled robot image transmission and pro- significance for improving the practical application value of multimedia enhancement technology. cessing system has high practicability. However, the appli- As an important means of comprehensive processing of cation in multimedia enhancement technology has not been media information, multimedia enhancement technology effectively applied. In order to enhance the application value has always been the research direction of many scholars. Jan of image processing variational model in multimedia en- et al. proposed a histogram-based energy saving algorithm to hancement technology and realize two-way development, its improve the resolution and image quality of modern mul- application research is very important. Based on the network-controlled robot image trans- timedia devices [1]. Zhang and Huo proposed a multimedia enhancement technique with quantum chaotic graph, which mission and processing system, this paper constructed a new image processing variational model, which was applied to encrypted the image and improved the reliability and se- curity of multimedia images [2]. To achieve the best mul- the multimedia enhancement technology. *rough the ap- plication test, it can be seen that the image processing timedia enhancement layer compression efficiency, Hoangvan et al. proposed a novel HEVC-based framework function of the model in this paper was ideal. *e total with high-quality scalability [3]. Ahmed et al. proposed a refresh time in mode 1 was 0.9s, and the refresh time in method for multisaliency enhancement for multimedia mode 4 was 1.13s. In the performance test, the variational image location estimation, which was mainly performed by model in this paper had better spatial adaptability. *e mean-shift clustering of visual words and saliency maps of memory usage had been stable in the range of 38.4%, and the images [4]. Ravisankar et al. used multiresolution sharpened CPU usage had only fluctuated in the range of 6.9%. In the load capacity test, the comprehensive value of the model images for reconstruction enhancement. Also, it was proved by performance measurement that this unsharp masking under the full-function operation of multimedia enhance- ment technology was 42.04%, while the comprehensive value method outperformed other enhancement techniques [5]. Heindel et al. proposed a lossy-to-lossless scalable multi- of the test under partial-function operation was 20.92%. *e load capacity of the model is lower than that of the tradi- media coding system. Scalability was achieved using lossy base layers combined with lossless compression of recon- tional model, but the load capacity is higher. From this point struction errors in enhancement layers [6]. *e development of view, the model constructed in this paper had ideal of image processing requires a higher level of multimedia practicability in the application of multimedia enhancement enhancement technology, and the previous research technology. methods are still lacking in efficiency. *e image processing variational model based on the network control robot image 2. Application of Image Processing transmission and processing system can play a strong ad- Variation Model vantage in its application. At present, the variational model in the network-con- 2.1. Multimedia Enhancement Technology. Multimedia en- trolled robot image transmission and processing system has hancement technology is an emerging technology developed been widely used in many fields. Zhang et al. used a vari- on the basis of the original multimedia technology. It can be ational model for network-controlled robotic image pro- regarded as a small embedded technology system integrating cessing in the linearization enhancement of elastic image hardware and software [13]. *is technology requires the co- denoising models. Simple complexity analysis was also design of software and hardware, so that the hardware and performed [7]. Sridevi and Srinivas Kumar efficiently software components can be divided reasonably. In multi- characterized intensity changes in images using a robust media enhancement techniques, nodes can be distributed in image processing variational model based on fractional or around a perceptual image object in a number of different nonlinear diffusion driven by differential curvature of the ways, such as random distribution and artificial positioning. image processing system [8]. Feng proposed a new varia- *ese nodes form technology networks, often through self- tional model under a network-controlled robot image organization. In terms of network functions, each node not transmission and processing system, which was used for only takes on the dual roles of terminal and router of tra- anti-noise document image binarization. Also, the noise and ditional multimedia network nodes but also performs image illumination robustness of the method was verified [9]. Li data acquisition, storage, management, and fusion [14]. In and Yang proposed adaptive variational functionals for special cases, it is also necessary to cooperate with other image inpainting, which demonstrated a stable variational nodes. *e sink node not only needs to have the function of scheme based on image processing systems. *e existence controlling other devices but also needs to have the functions and uniqueness of minimizing functional solutions were also of establishing a routing table for routing selection, infor- investigated [10]. Combining the advantages of recently mation forwarding, data fusion, and storing image infor- developed robotic image transmission and processing sys- mation of other nodes. Its technical framework is shown in tems, Liu proposed a new variational model for solving the Figure 1. challenging problem of cartoon texture image decomposi- A large number of nodes are distributed in or around the tion [11]. To maximize adaptability, Suwanwimolkul et al. monitoring area in Figure 1. *ese nodes consist of self- proposed a new sparse signal estimation for robotic imaging organizing forms. *e data monitored by the technology systems. Among them, noise and signal parameter node are passed adjacently on other nodes. During Journal of Robotics 3 Sink node Node Communications network Communication link Detection area management node Data management Figure 1: Multimedia enhancement technology framework. transmission, the image data are simultaneously enhanced it is very important to introduce the image processing by multiple nodes and then returned to the summary point variational model of the network-controlled robot image (sink node) through multiple hops. After processing, they transmission and processing system to deal with the reduced transmission and processing of image data under the are finally transmitted to the management node [15]. *e multimedia enhancement technology mainly uses man- background that the nodes transmit information and re- agement nodes to enhance and control image information. sources cannot be used. Image information processing plays a very important role in multimedia enhancement technology. However, with 2.2. Variational Model of Image Processing. *e network the increasing amount and variety of image data, its func- remote control robot is different from the traditional robot; tions face huge challenges. it refers to the robot that can realize the remote operation (1) *ere are effects of energy consumption and com- under the network control. Its operating subject is generally munication delays. Multimedia enhancement tech- a professional. In the control environment, professionals use niques need to take into account the excess energy position sensors, visual feedback, and other means to achieve consumed by excess runtime. *erefore, people have remote network control of the system. *e research on developed a method of hibernation. However, even remote control has been in the exploratory stage since the with this method, there is no guarantee that the beginning of the 1980s. It has flourished with the rise of consumption is reduced. computer network technology [16]. With the development of computer network, remote control is no longer restricted (2) *ere is no fixed time for information transmission by geographical conditions. *e emergence of the com- of multimedia enhancement technology. Without a munication network reduces the communication cost, fixed time requirement, multiple kinds of data are thereby improving the high cost problem caused by the transmitted at the same time, which makes it difficult design of dedicated connection lines in the traditional for the information receiver to process so much technology. Under the requirements of the development information at the first time. environment of the times, network-controlled robots have (3) It is impossible to handle the performance standards become an important direction in current robotics research. at the same time. *e main difficulty is the confusion *e image transmission and processing system is the key of the dormancy mechanism, which makes the in- system for the network control robot to realize the image formation transmission not timely. Nowadays, task processing function. Generally, a camera equipped with multimedia enhancement technology only considers a computer is used to take pictures of the outside scene. In the optimization of one performance index, while the initial stage of the transmission of image data, the circuit ignoring the optimization of other performance is switched by the control signal. *e differential signal is indicators at the same time. converted into a digital image signal in a standard format *e multimedia enhancement technology manages the and then sent to the control unit for execution and pro- relevant image information and data due to those competing cessing. When the control unit realizes the processing function and the synchronization signal transmission nodes. Once it fails, it is difficult for other applications in the entire framework to continue to provide services. *erefore, function at the same time, the image data that has been 4 Journal of Robotics initially processed is transmitted to the system memory for In this case, the additive noise ε satisfies 2 T buffering, so as to prepare for the needs of subsequent work. ε ∼ N(0, σ , D D ), resulting in k k k By waiting for the system command to be sent, the image ∗ 2 T (3) p v |v ∼ Nv ,σ , D D . data are sent to the converter together with the control signal k k k k k for conversion and converted into a specified analog T T T ∗ ∗ ∗ ∗ ∗ Let t � (v , −v ) � (t , t ) and t � (v , −v ) � 2 1 1 2 2 1 quantity according to the command requirements. Finally, (t , t ) ; then, there is 1 2 after the analog quantity is encoded and data are com- ∗ ∗ ∗ 2 T pressed, they are output through microwave. *e hardware p t |t ∼ p t |t , p t |t ∼ NHt ,σ , D D . k k k k k 3−k 3−k structure of the system is shown in Figure 2. *e exterior image captured by the camera is disturbed (4) by noise in the process of formation and transmission. After It is assumed that the tangent vector field t of the image is the image data are transmitted in real time, in order to ensure the image quality during subsequent processing, the piecewise smooth, so that the prior information of the total variation is adopted, namely, system needs to perform basic processing through the image processing variational model in the control module. − α p(t|α)∝ e Ω|∇t|dx. *erefore, the image processing variational model mainly (5) completes the overall control of the image, and the signal flowchart is shown in Figure 3. Among them: *e image processing of multimedia enhancement 2 2 2 2 2 (6) |∇t| � D t + D t + D t + D t . technology usually relies on software to complete. However, 1 1 2 1 1 2 2 2 with the continuous development of computer technology, *e estimation of t uses the maximum a posteriori es- real-time processing and transmission of image data based timation method, that is, maximization p(t|t ). It can be on algorithmic models is a new research direction [17]. *e ∗ ∗ ∗ seen from the formula that p(t|t ) � p(t|t )p(t)/p(t ). model is based on a network-controlled robot image *erefore, maximizing p(t|t ) is equivalent to minimizing transmission and processing system, which realizes real- the log-likelihood function −log p(t|t ), as shown in time processing of image data, and it has good feasibility. Figure 4. *e image processing variational model is applied in the *e variational problem can be obtained by further multimedia enhancement technology, which can greatly derivation [19]: shorten the operation time of image processing, and it has a very good enhancement effect. η 2 T ∗ min Ω|∇t|dx + ΩD Ht − t dx. k (7) 3−k k *erefore, this paper uses the image processing varia- ∇·t�0 tional model to solve the image processing problem under the multimedia enhancement technology. *e variational Among them, incompatibility condition ∇ · t � 0 is used, model consists of two steps: estimating the tangent vector and η � σ /α. field of the image and reconstructing the original image from It is noted that the difference operator appears in the the estimated tangent vector field and the blurred image. *e second term of formula (7). *erefore, formula (7) itself is in application of variational model in multimedia enhance- the form of a variational model, which is more reasonable for ment technology is expounded from the discrete point of the estimation of tangent vector fields. However, due to the view. existence of the first-order difference operator D , the so- First, the estimation of the tangent vector field in lution of the variational problem is not unique, and the 2 T multimedia enhancement techniques needs to be consid- solutions differ by a constant. Since ε ∼ N(0, σ , D D ), k k k ered. *e image collected in the image transmission and there is E(v ) � Hv according to the formula. *erefore, processing system of the network-controlled robot is additional constraints can be added to the tangent vector regarded as a two-dimensional surface defined on the area field t, resulting in the following minimization problem: Ω � [1, N ] × [1, N ]. *e parameter settings are shown in 1 2 η 2 T ∗ Table 1. min Ω|∇t|dx + ΩD Ht − t dx. 3−k k k (8) t∈U 2 Among them, the normal vector and tangent vector of k the image are denoted as n � ∇u � (D u, D u) and 1 2 T Among them, the function space t � ∇ u � (D u − D u) . *e difference operator is applied 2 1 U � t|∇ · t � 0, ΩHt � Ωt . Unlike formula (7), it can on both sides of the formula to obtain [18] be proved that when H is injective, the solution to the variational problem is unique, so formula (7) is used to D u � D Hu + D ε, k � 1,2, (1) k k k estimate the tangent vector field t. *e distance of l between the unit image gradient and where H is the block circulant matrix corresponding to h. the estimated unit normal vector field is used as the regu- ∗ ∗ Let D u � v D u � v , D ε � ε , and the operator is k k k k k larization term, resulting in the minimization problem of commutative in multimedia enhancement technology, so image restoration [20]: v � Hv + ε . (2) k k k Journal of Robotics 5 image data image data image data Control Storage module module control Camera Interface conversion signal Encoding and Compression Module Microwave antenna Figure 2: Hardware structure of image transmission and processing system. Storage Encoding and Compression module Module image data image data image data clock signal clock signal clock signal Image Processing Start collecting signals Variational Models sync signal sync signal Figure 3: Signal flowchart. Table 1: Parameter setting and its interpretation. Sequence Parameter setting Meaning 1 D First-order difference operator in the horizontal direction 2 D First-order difference operator in the vertical direction 3 n *e normal vector of the image 4 t Tangent vector of the image 0.0020 ∇u n λ (9) min Ω − dx + Ω Hu − u dx. u |∇u| |n| 2 *e regular term in formula (9) can be expressed as 0.0015 ���������� √��������� ∇u n ∇u · n Ω − dx � Ω 2 − 2 dx � Ω 2 − 2 cos θdx. |∇u| |n| |∇u||n| 0.0010 (10) It can be seen that it is also only directionally matched. However, problem (9) is still nonconvex. *erefore, the 0.0005 following iterative formula is introduced to approximate this problem [21]: 0.0000 ∇u n λ k+1 ∗ u � argmin Ω − dx + Ω Hu − u dx. |∇u| |n| 2 0.0 0.2 0.4 0.6 0.8 (11) Theta Figure 4: Maximum a posteriori estimation legend. Likelihood function value 6 Journal of Robotics k k Formula (11) is a convex function. w � 1/|∇u | is taken Further analysis of this variational model shows that k k as the edge indicator function. When |∇u | � 0, w is a fixed since incompatibility condition ∇ · t � 0 is used in the first constant; then, formula (11) is essentially a spatially adaptive step, there is an image u that satisfies image processing variational model. Compared with the ∇u � n. (12) traditional variational model, the model here can better preserve the details and texture information of the image. k Assuming |∇u | ≈ |n|, the right side of formula (12) can *erefore, compared with the previous regularization terms be expressed as 8 and 9, the model proposed here has better performance in multimedia enhancement techniques. λ λ 2 2 ∗ k ∗ min Ω|∇u − n|dx + Ω Hu − u dx � min Ωw ∇u − ∇u dx + H u − u − u − Hu 1 1 1 u 2 u 2 (13) 2 k ∗ � min Ωw ∇u dx + Ω Hu − f dx. 2 2 u 2 ∗ ∗ Among them, f � u − Hu . *e minimization value data processing and system control software to facilitate the u is obtained by formula (13); then, the restored image can operation of the variational model of image processing. *e be expressed as Intel Core i7 chip and 8GB memory can realize high-speed operation on a large amount of RGB image data and depth u � u + u . (14) 1 2 image data, which can meet the real-time requirements of the network control robot for the image acquisition and *erefore, the proposed two-stage model can be processing system. *e USB 3.0 interface can meet the in- explained as shown in Table 2. terface requirements of multimedia enhancement technol- *e minimization problem of direction matching is ogy. *erefore, the test environment of this paper meets the extended to deblurring clarity, and its corresponding un- test conditions. constrained problem can be expressed as 2 ∇u n λ ∗ 2 (15) min Ω − dx + Ω Hu − u dx. 3.1. Functional Test. In this paper, the application test of |∇u| |n| 2 image processing variational model in multimedia en- A convex problem can also be obtained by linearizing it hancement technology includes two categories, namely, as in formula (11). Assuming |∇u | ≈ |n|, according to functional test and performance test. Functional testing is a formulas (12) and (13), the following minimization problem necessary testing method. To ensure the effectiveness of the for u can be similarly obtained: image processing variational model in multimedia en- hancement technology, the first thing that needs to be en- 2 2 k ∗ min Ωw ∇u dx + Ω Hu − f dx. (16) sured is its realization effect in the image processing 2 2 2 2 function. *is paper observes the task refresh time of the two types of models in multimedia enhancement technology By observing formula (16), it is found that the mini- under different task modes, and the results are shown in mization problem reduces to a weighted image variational Figure 5. processing model. Obviously, this model can better preserve *e image processing function mainly analyzes the the discontinuous information of images in the compression original and panoramic images through the variational processing of multimedia enhancement technology and model, which is convenient for the follow-up inspection of maintain higher data transmission quality and efficiency. the multimedia enhancement technology program. *e stored size is 512 ×512 ×2 original 16 bit image and 3. Application Testing 14212 ×480 panorama image. *e detection frequency is the same as the tracking frequency. *e number and complexity In order to verify the effectiveness of the image processing variational model based on the network-controlled robot of tasks that need to be processed in different modes of multimedia enhancement technology are also different. In image transmission and processing system in multimedia enhancement technology, this paper compares its applica- general, the more tasks that are processed and the higher the tion effect with the traditional image processing variational complexity, the longer the refresh time is. A delay that is too model in multimedia enhancement technology. *e tests are different leads to a significant decrease in the functionality of carried out from these aspects, respectively, and the test the multimedia enhancement technology, resulting in a low environment is shown in Table 3. degree of image processing. *is article sets mode 1 as the From Table 3, the computer with Windows 11 operating easiest mode and mode 4 as the most complex mode. As can system has corresponding mature interfaces and drivers and be seen from Figure 5(a), in mode 1, the detection refresh a lot of development experience to use. It also supports other time of the image processing variational model in the Journal of Robotics 7 Table 2: Interpretation of the two-stage model. Object Model classification Sequence Effect 1 Restore multimedia image u Variational processing u preserves the discontinuity of the image gradient information and the *e proposed two-stage models 2 smooth areas of the image model Weighted variational 3 Restore multimedia image u model 4 u preserves the discontinuous information of the image itself Table 3: Experimental test environment. On the whole, the stability of the traditional image processing variational model in the application of multi- Sequence Equipment Specification media enhancement technology is not ideal, and it fluctuates Intel Core i7 1 Computer greatly with the change of sampling frequency. In this RAM 8GB context, the normal function of the multimedia enhance- Windows 11 2 Operating system ment technology is very likely to be abnormal, which causes 64 bit it to be in a state of failure. *e variational model in this 3 Interface USB 3.0 paper has better spatial adaptability, which can completely retain image information under the condition of increasing occupancy and usage. multimedia enhancement technology was 0.43s, the track- Under different working modes, the performance of ing refresh time was 0.47, and the total refresh time was 0.9s. the model is quite different. *erefore, under the same At this time, the model refreshes the image without delay. In mode 4, the test scenario is multitasking and high load. At working mode, this paper observes the load capacity generated by full-function operation and partial-func- this time, the detection refresh time was 0.51s, and the tracking refresh time was 0.62s. In Figure 5(b), the detection tion operation under different pressures, as shown in Figure 7. refresh time of mode 1 was 0.46s, and the tracking refresh time was 0.51s. In mode 4, the detection refresh time was *e load capacity test under different stress conditions in Figure 7 only compares the normal stable condition, 0.73s and the tracking refresh time was 0.71s. It can be seen which does not consider the failure condition. It is not that it has the long time and high latency compared to the difficult to see that there is a big difference between the traditional variational model. *e image processing varia- performance of full-function operation and partial- tional model based on the network control robot image function operation. *e main reason is that the multi- transmission and processing system has a short refresh time media enhancement technology adopts multithreaded in the multimedia enhancement technology and basically has no delay, which can meet the practical application. processing under full-featured operation, and the load is also high. While some functions are running, most of the technology modules are in an idle state. As can be seen 3.2. Performance Test. Performance testing is an important from Figure 7(a), the image processing variational model part of application testing. Under different sampling fre- in this paper had a better load capacity in the multimedia quencies, the stability of multimedia enhancement tech- enhancement technology, and the test comprehensive value under full-function operation was 42.04%. Also, the nology is greatly challenged. At different frequencies, the test is carried out by observing the occupancy rate and usage rate test comprehensive value under partial-function opera- tion was 20.92%. In Figure 7(b), the test comprehensive of different image processing variational models in the application of multimedia enhancement technology to value of the traditional image processing variational hardware devices. *e results are shown in Figure 6. model under full-function operation reached 53.94%, and In Figure 6, different sampling frequencies are set in this the test comprehensive value under partial-function op- paper, but they are all within the range of normal working eration reached 26.72%. *is may be related to the in- conditions. It can be seen from Figure 6(a) that under ability of variational models to compress image different sampling frequencies, the memory usage of the information efficiently. If the compression is not per- model in this paper in the application of multimedia en- formed correctly, not only the key information cannot be hancement technology had been in a stable range of 38.4%. completely output but also the technical load pressure is too large, which affects the follow-up work. *e image *e CPU occupancy rate fluctuated with the change of sampling frequency. As the sampling frequency increased, processing variational model based on the network- controlled robot image transmission and processing the occupancy rate also increased, from 47.2% to 54.1%, an increase of 6.9%. In Figure 6(b), both the memory usage and system can better preserve the discontinuous information CPU usage of the traditional model had changed. Memory of the image in the multimedia enhancement technology. usage increased from 39.1% to 42.7%, and CPU usage in- Also, it is effectively compressed to maintain a relatively creased from 47.5% to 56.2%, a change of 8.7%. ideal load effect. 8 Journal of Robotics 0.7 0.8 0.73 0.71 0.62 0.7 0.6 0.56 0.54 0.61 0.51 0.58 0.6 0.57 0.49 0.5 0.47 0.47 0.51 0.52 0.43 0.5 0.46 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 Pattern 1 Pattern 2 Pattern 3 Pattern 4 Pattern 1 Pattern 2 Pattern 3 Pattern 4 PATTERN CLASSIFICATION PATTERN CLASSIFICATION Detection Detection Tracking Tracking (a) (b) Figure 5: Task refresh time test results. (a) *e refresh time of the model in this paper. (b) *e refresh time of the conventional model. 42.7 38.4 220 220 56.2 54.1 38.4 42.3 54.7 53.2 41.6 38.4 52.1 51.8 39.1 38.4 51.3 51.4 39.1 38.4 48.7 48.3 39.1 38.4 47.5 47.2 0 20 40 60 0 20 40 60 Test results (%) Test results (%) Memory usage Memory usage CPU usage CPU usage (a) (b) Figure 6: Occupancy and usage test results. (a) *e occupancy rate and usage rate under this model. (b) *e occupancy rate and usage rate under the traditional model. REFRESH TIME (S) Sampling frequency (HZ) REFRESH TIME (S) Sampling frequency (HZ) Journal of Robotics 9 50 75 42.7 42.4 42.4 42.1 40.6 55.5 54.8 40 60 53.9 53.4 52.1 30 45 21.4 21.2 20.7 20.8 20.5 27.9 27.3 26.8 26.2 25.4 20 30 10 15 0 0 10 30 50 70 90 10 30 50 70 90 Stress environment Stress environment Full Function Full Function Part of the function Part of the function (a) (b) Figure 7: Load capacity test results. (a) *e load capacity of this model. (b) *e conventional model load capacity. 4. Conclusion Data Availability As a comprehensive form of multimedia information pro- *e data used to support the findings of this study are cessing, multimedia enhancement technology is the product available from the corresponding author upon request. of the combination of communication technology and continuous development of multimedia technology. It is of Conflicts of Interest great practical significance to improve its application value for improving the complex information environment of *e authors declare that they have no conflicts of interest. multimedia. In this paper, a new image processing varia- tional model was proposed on the basis of the image References transmission and processing system of the network-con- trolled robot. Also, it was applied in multimedia enhance- [1] L. M. Jan, F. C. Cheng, and C. H. Chang, “A power-saving ment technology, which effectively improved the quality and histogram adjustment algorithm for OLED-oriented contrast effect of information transmission. In addition, under the enhancement,” Journal of Display Technology, vol. 12, no. 4, condition of ensuring that the information resources were pp. 368–375, 2017. fully reserved, the consumption has been reduced, and the [2] J. Zhang and D. Huo, “Image encryption algorithm based on technical performance and practical effect have been im- quantum chaotic map and DNA coding,” Multimedia Tools proved. However, in the testing process, it is found that the and Applications, vol. 78, no. 11, pp. 15605–15621, 2019. [3] X. Hoangvan, J. Ascenso, and F. Pereira, “Adaptive scalable research in this paper still needs continuous improvement in video coding: an HEVC-based framework combining the practical application. Under different sampling frequencies, predictive and distributed paradigms,” IEEE Transactions on the occupancy performance of the model in this paper in Circuits and Systems for Video Technology, vol. 27, no. 8, technical applications has improved, but there are still pp. 1761–1776, 2017. certain fluctuations. Also, this paper did not consider the [4] S. Ahmed, K. G. Tan, M. Y. Alias, F. Hossain, application performance in different information environ- N. Z. A. Rahman, and L. C. Pao, “Circularly polarized high- ments. *ese two points still need to be continuously im- gain antenna using metasurface for C-band Application,” Far proved. In the future research work, it is necessary to East Journal of Electronics and Communications, vol.18, no. 7, continue to study the shortcomings of the image processing pp. 1099–1106, 2018. model and continuously improve its practical value in [5] P. Ravisankar, T. Sree Sharmila, and V. Rajendran, “Acoustic multimedia enhancement technology. It also makes full use image enhancement using Gaussian and laplacian of existing technology to improve research conditions and pyramid—a multiresolution based technique,” Multimedia quality. Tools and Applications, vol. 77, no. 5, pp. 5547–5561, 2018. Test value (%) Test value (%) 10 Journal of Robotics [6] A. Heindel, E. Wige, and A. Kaup, “Low-complexity en- hancement layer compression for scalable lossless video coding based on HEVC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 8, pp. 1749–1760, [7] J. Zhang, R. Chen, C. Deng, and S. Wang, “Fast linearized augmented Lagrangian method for euler’s elastica model,” Numerical Mathematics: 3eory, Methods and Applications, vol. 10, no. 1, pp. 98–115, 2017. [8] G. Sridevi and S. Srinivas Kumar, “Image inpainting based on fractional-order nonlinear diffusion for image reconstruc- tion,” Circuits, Systems, and Signal Processing, vol. 38, no. 8, pp. 3802–3817, 2019. [9] S. Feng, “A novel variational model for noise robust document image binarization,” Neurocomputing, vol. 325, pp. 288–302, [10] S. Li and X. Yang, “Novel image inpainting algorithm based on adaptive fourth-order partial differential equation,” IET Image Processing, vol. 11, no. 10, pp. 870–879, 2017. [11] X. Liu, “A new TGV-gabor model for cartoon-texture image decomposition,” IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1221–1225, 2018. [12] S. Suwanwimolkul, L. Zhang, D. Gong et al., “An adaptive markov random field for structured compressive sensing,” IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1556–1570, 2019. [13] X. Qian, H. Wang, Y. Zhao et al., “Image location inference by multisaliency enhancement,” IEEE Transactions on Multi- media, vol. 19, no. 4, pp. 813–821, 2017. [14] M. Talaat, M. Tayseer, and A. Elzein, “Digital image pro- cessing for physical basis analysis of electrical failure fore- casting in XLPE power cables based on field simulation using finite-element method,” IET Generation, Transmission & Distribution, vol. 14, no. 26, pp. 6703–6714, 2020. [15] K. Deng, L. Yuan, Y. Wan, and J. Pan, “Optimized cross-layer transmission for scalable video over DVB-H networks,” Signal Processing: Image Communication, vol. 63, no. 9, pp. 81–91, [16] T. Ohbitsu, “Information processing apparatus, information processing system, recording medium, and method for transmission and reception of moving image data,” 3e Plant Cell Online, vol. 2574, no. 12, pp. 356–362, 2017. [17] H. Rahimi Nohooji, I. Howard, and L. Cui, “Neural network adaptive control design for robot manipulators under velocity constraints,” Journal of the Franklin Institute, vol. 355, no. 2, pp. 693–713, 2018. [18] S. Lathuiliere, B. Masse, and P. Mesejo, “Neural network reinforcement learning for audio-visual gaze control in hu- man-robot interaction,” Pattern Recognition Letters, vol. 118, pp. 61–71, 2018. [19] D. Qin, A. Liu, D. Zhang, and H. Ni, “Formation control of mobile robot systems incorporating primal-dual neural net- work and distributed predictive approach,” Journal of the Franklin Institute, vol. 357, no. 17, pp. 12454–12472, 2020. [20] R. Ojstersek, A. Javernik, and B. Buchmeister, “*e impact of the collaborative workplace on the production system ca- pacity: simulation modelling vs. real-world application ap- proach,” Advances in Production Engineering & Management, vol. 16, no. 4, pp. 431–442, 2021. [21] T. Sari, H. K. Gules, and B. Yigitol, “Awareness and readiness of Industry 4.0: the case of Turkish manufacturing industry,” Advances in Production Engineering & Management, vol. 15, no. 1, pp. 57–68, 2020.
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