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Graphic design, as a product of the burgeoning new media era, has seen its users' requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image's color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%–10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design. [ABSTRACT FROM AUTHOR]

To enhance the segmentation accuracy of car front face elements such as headlights and grilles for car front face design, and to improve the superiority and efficiency of solutions in automotive partial modification design, this paper introduces MD-TransUNet, a semantic segmentation network based on the TransUNet model. MD-TransUNet integrates multi-scale attention gates and dynamic-channel graph convolution networks to enhance image restoration across various design drawings. To improve accuracy and detail retention in segmenting automotive front face elements, dynamic-channel graph convolution networks model global channel relationships between contextual sequences, thereby enhancing the Transformer's channel encoding capabilities. Additionally, a multi-scale attention-based decoder structure is employed to restore feature map dimensions, mitigating the loss of detail in the local feature encoding by the Transformer. Experimental results demonstrate that the MSAG module significantly enhances the model's ability to capture details, while the DCGCN module improves the segmentation accuracy of the shapes and edges of headlights and grilles. The MD-TransUNet model outperforms existing models on the automotive front face dataset, achieving mF-score, mIoU, and OA metrics of 95.81%, 92.08%, and 98.86%, respectively. Consequently, the MD-TransUNet model increases the precision of automotive front face element segmentation and achieves a more advanced and efficient approach to partial modification design. [ABSTRACT FROM AUTHOR]

Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model's superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image. [ABSTRACT FROM AUTHOR]

Graphic design, as a product of the burgeoning new media era, has seen its users’ requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image’s color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%–10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design.

Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model’s superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image.

Optical processing technology can improve the accuracy and efficiency of image processing. Visual packaging art design simulation is to quickly generate realistic packaging effects in the early stage of packaging design, so as to make better design decisions. Traditional simulation methods often require a lot of time and human resources, and the results lack realism and detail. Machine vision technology based on optical processing can make full use of image processing algorithms and computer vision technology to achieve efficient and accurate packaging design simulation. In this paper, a machine vision technology framework based on image optical processing is proposed. In the image acquisition stage, high-resolution cameras are used to acquire real packaging images, and in the optical processing stage, algorithms are used to enhance, reduce noise and correct images to improve image quality and clarity. In the image analysis stage, machine learning algorithms are used to extract features and detect and identify packaging elements, providing data support for subsequent simulation rendering. The results show that the machine vision technology based on image optical processing can generate high quality and realistic simulation effect of packaging design. Compared with traditional methods, this technique has achieved significant improvements in terms of time cost and design quality. [ABSTRACT FROM AUTHOR]

Art design is a method of conveying human feelings and emotions, particularly via the use of visual structures such as paintings or sketches. Every element of our life, including arts and crafts, has been positively affected by the introduction of novel technologies such as artificial intelligence (AI) and machine learning (ML). In today's modern environment, these technologies have altered the techniques of art creation, consumption, and distribution. In today's environment, ML and human emotions are the two most important aspects for interactive and high‐quality art design. Whereas traditional learning systems can be extremely effective in the teaching and learning process of art‐related subjects, AI and ML can be very effective in the teaching and learning process of art‐related subjects for the advancement of learners' artistic skills. The productive and active role of AI and ML in the developments and advancements of art design has been given a very comprehensive and detailed overview in this study. Following a detailed examination of the existing techniques, distinct characteristics have been identified. Six of the most widely utilized features were chosen from the literature to execute the analytical hierarchy process (AHP). For ranking the options based on the weights derived by AHP, the TOPSIS algorithm is used. The option with the best performance came in first, whereas the one with the worst performance came in last. [ABSTRACT FROM AUTHOR]

The article presents an analysis of the artistic and graphic design of the only satirical and caricature magazine of printing and bookbinding workers «Balda» published in Russia. As result of the all the published issues, the reader’s address of the magazine was identified, as well as its technical characteristics were determined. After studying all three issues of the magazine, it was revealed how much textual information was printed, how much textual information was accompanied by drawings and how many percent of the magazine contained drawings as independent material. The features of graphic and compositional techniques used in the design of the magazine are analyzed. The article identifies the main features of the title complex “Baldy”, describes the fonts used. Fonts definitely have which group they belong to, font size, font type, density, saturation. The graphic and decorative elements used in the design of the magazine are described, with the help of which its original and memorable style is formed. Only three issues of «Balda» magazine were published: on January 1, 1907 – No. 1, on January 9, 1907 – No. 2, on January 16, 1907 – No. 3. Some of the magazine’s circulations were confiscated. Despite the fact, that the arrest of the magazine was later lifted, the editorial board decided to close the magazine «Balda» by starting to publish the satirical, humorous and caricature magazine of the workers of the printing and bookbinding business «Topor». There were two numbers that were confiscated later too. The purpose of the study is to identify trends in the artistic and graphic concept of specialized magazines in order to develop quality criteria for relevant publications. The method and methodology of the work: the basis of the research is the principle of a systematic approach, as well as matrix, analytical, thematic and typographic methods. The practical significance of this work lies in the possibility of using the data obtained when creating new journals, as well as for adapting existing ones to modern requirements. In pre-revolutionary Russia, many specialized magazines were published intended for workers in the printing industry and bookbinding but only «Balda» and «Topor» magazines remain the only humorous, satirical and caricature magazines, which makes them a unique source of both historical, cultural and artistic information.

To enhance the segmentation accuracy of car front face elements such as headlights and grilles for car front face design, and to improve the superiority and efficiency of solutions in automotive partial modification design, this paper introduces MD-TransUNet, a semantic segmentation network based on the TransUNet model. MD-TransUNet integrates multi-scale attention gates and dynamic-channel graph convolution networks to enhance image restoration across various design drawings. To improve accuracy and detail retention in segmenting automotive front face elements, dynamic-channel graph convolution networks model global channel relationships between contextual sequences, thereby enhancing the Transformer’s channel encoding capabilities. Additionally, a multi-scale attention-based decoder structure is employed to restore feature map dimensions, mitigating the loss of detail in the local feature encoding by the Transformer. Experimental results demonstrate that the MSAG module significantly enhances the model’s ability to capture details, while the DCGCN module improves the segmentation accuracy of the shapes and edges of headlights and grilles. The MD-TransUNet model outperforms existing models on the automotive front face dataset, achieving mF-score, mIoU, and OA metrics of 95.81%, 92.08%, and 98.86%, respectively. Consequently, the MD-TransUNet model increases the precision of automotive front face element segmentation and achieves a more advanced and efficient approach to partial modification design.

The mental model of art design in the realm of virtual reality (VR) technology is a dynamic fusion of creativity, immersion, and technological innovation. In this paradigm, artists leverage the immersive capabilities of VR to transcend traditional boundaries, creating a multisensory experience that goes beyond the confines of a two-dimensional canvas. The artist's mental canvas extends into a threedimensional space where users can engage with art on a profound level, exploring and interacting with the artwork in ways previously unimaginable. The core objective is to redefine art design design through the lens of machine vision cognition into the ethical and social implications inherent in Art Design. With the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO) to amplify the capabilities of VR systems. At the forefront of this study is the reimagination of art design design, characterized by aesthetics, ergonomics, and functionality. The infusion of machine vision cognition into these designs not only enhances user experience but also prompts contemplation of ethical considerations surrounding privacy, accessibility, and informed consent. Ethical and social implications are scrutinized comprehensively, acknowledging the profound impact of VRs on individual rights, security, and privacy. The research probes into equitable access to VR technologies, ethical data utilization in art design, identity verification, and surveillance contexts. Central to this multidisciplinary inquiry is the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO). With swarm intelligence and probabilistic modelling, HMPSO enhances the efficiency, accuracy, and reliability of Mental Model VR systems. It addresses the critical challenge of reducing false positives and false negatives in VR authentication. The research methodology comprises performance evaluations, ethical analyses, and socio-cultural investigations, offering a comprehensive view of the interplay between design innovation, machine vision cognition, ethical awareness, and the application of HMPSO in the Art Design. [ABSTRACT FROM AUTHOR]

In this paper, the 15 original variables of the Airpak-based influence factor scale for art and design talent training were first determined, and the five-level scoring method was applied to score the 15 influence factors. Then, we used factor analysis to factorize the data, standardize the sample data, construct a coordinate system with common factors, and use the projection of each variable in the coordinate system instead of the original variables to construct the correlation coefficient matrix R. Then, we solved the correlation coefficient matrix between each variable, selected the number of common factors, and calculated the eigenvectors of the factor loading matrix A to construct the influence factor analysis model. Finally, the model is used to analyze the weight of each influencing factor of Airpak-based art and design talent cultivation and to build an innovative path for talent cultivation. In terms of students’ own factors, the influence weight of learning enthusiasm on talent cultivation is 0.48, and the influence weights of learning achievement and design talent are 0.43 and 0.545, respectively; in terms of teaching level factors, the influence weights of teachers’ teaching ability, professional level and design experience on talent cultivation are 0.44, 0.5 and 0.37 respectively. The research of this paper has an important reference and reference value for the cultivation of art and design talents.

The first part of this paper examines the aesthetic and application advantages of art design using human-computer interaction technology and develops a multimodal perceptual human-computer interaction system for art design. Multimodal data is obtained using multi-scale convolutional kernels for acoustic feature extraction and deep convolutional neural networks for multiple interaction image feature fusion. Finally, a test analysis is conducted to verify the system's effectiveness in this paper. According to the results, the system has an average wake-up success rate of 99.51% and a wake-up response time of 0.3665 seconds. Implementing human-computer interaction technology and deep neural networks in art design is effective and promotes the development of art design.

This paper establishes the general architecture of the teaching resource repository through the design of course clusters and specific course resource organizations for the teaching characteristics of art and design majors and builds the business process from two aspects: teacher course resource archiving and student assignment management. For the association of knowledge points of teaching resources in the repository, the coding of text semantics is completed based on BERT, the text data is enhanced by a two-way maximum matching algorithm, and the enhanced data is input into the LSTM-CRF model for training to achieve entity prediction of knowledge points. For the recognition of art design teaching resources, the F1 score of this paper’s method is improved by 9.56% compared with the ATT-CNN model and 2.65% compared with the currently known better model ATT-BLSTM, which can better characterize the semantic information of the text.

This paper first investigates cognitive psychology and art design, divides cognitive memory into sensory memory, short-term memory and long-term memory according to cognitive psychology, and investigates art design's needs, psychology and creative thinking ability. Then, an art design is proposed that uses image processing technology. The first step in designing an art design using image processing technology is to use an image processing algorithm. Based on the image luminance equalization processing in art design, image fusion and wavelet noise reduction processing are carried out, and ANOVA cognitive psychology and art design experimental data are used to realize art design human-computer interaction. The results show that the analysis of the model with artistic design creative thinking variability as the dependent variable found that the R2 of the model was 0.036, and the adjusted R2 was 0.023. F=2.633, p=0.015, and the grade level had a significant effect on artistic design creative thinking variability. This study is a comprehensive and thorough cognitive psychology and art and design relationship that can contribute to development.

Based on big data analysis, this paper clarifies the basic elements and other elements in visual communication through the expression of visual communication elements in art design and constructs the formation mechanism of visual art design and the characteristics of information cognition. Based on the image processing algorithm for image brightness equalization processing, the image fusion and wavelet noise reduction processing are combined with the pixel point quantization tracking method, and specific cases demonstrate the optimization effect and practicality. The results show that in the example solution, the visual communication index of each module is Z = 4289, while the optimized visual communication index Z&prime = 4834 is obtained after processing by the image-based processing algorithm, which improves the communication effect and quality of the art design. This paper proposes a new idea and method that provides inspiration and reference for art design practice and research.

This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific cultivation path for art design professionals under the industry-teaching fusion mode with respect to the cultivation value of industry-teaching fusion for art design professionals. The association rule algorithm is selected to set up art design professional courses, and the neural network algorithm is selected to establish the talent cultivation mechanism model of industry-teaching fusion under the background of big data. According to the association rules for talent training path effect prediction, and then use big data is used to analyze the demand characteristics of art design professional talent training under the fusion of industry and education based on the demand characteristics for targeted talent training. The average difference between the student’s scores and the actual scores is 17.38, the accuracy rate of the model is 87.15% in the case of the allowable error range of 10 points, and the accuracy rate of the model is 90.55% in the case of the allowable error range of 15 points. A series of interventions, such as promotion rate prediction and academic warning, can be implemented based on this.

The essence of art design is the process of emotional interaction with humans. In this paper, emotions are classified using multimodal fusion features, and abstract fusion features are obtained by sampling the multimodal matching tensor using average pooling. The matching fusion matrix in the tensor operator is used to convert from two-modal to multi-modal matching. Virtual interaction model in art design controls the design objectives, and the optimized virtual world is constructed by using virtual reality technology, so as to build an immersive art design model. Finally, a study was conducted to examine the impact of the use of emotion perception and interaction technology in art product design with students from the School of Design and Art, University of G. The results show that the liking degree of three-color matching in the cognitive experiment test is 0.307, which is higher than the liking degree of two-color matching of 0.223, indicating that overall three-color matching samples are more popular in art design. This study provides effective evaluation and guidance for designing art products that are emotional and innovative.

The current art design teaching mode only focuses on the teaching of theoretical knowledge, ignoring the mining of user needs, which can’t make the art design recognized by users. This paper takes the Kano demand model as the basis, combines user demand for innovative art design teaching modes, and provides a related method of demand analysis. The process of art design teaching requires an in-depth analysis of landscape design needs, using cluster analysis methods to cluster design needs and optimization through GA indicators. Based on the Kano demand model, the landscape design evaluation elements are screened, and the data are quantitatively analyzed in terms of satisfaction and cluster analysis results with the landscape design of the ancient city of L as an example. The results show that the demand-oriented art design teaching focuses on grasping the use function and cultural perception of the landscape, and its demand intensity value is 0.68 and 0.89, respectively. Facing different activity characteristics, the users need to take different landscape design tendencies. Compared with the sports-oriented crowd, the entertainment-oriented crowd expects the cultural nature of the scene now sketches and ornaments and the coordination of the overall landscape, and the interval range of its Better coefficient is [0.3], [0.3], and the interval range is [0.3]. The range of the Better coefficient is [0.3,0.5]. Demand-oriented art design teaching mode can give students the ability to deeply explore the user’s needs so as to make the landscape design more in line with the user’s needs and improve the attractiveness of the landscape.

Art and design, as a professional field closely linked with market demand, is imperative to reform the teaching mode. In this paper, the concept of constructivism is incorporated into the professional education of art design, and a new teaching framework is established through the guidance of teaching concepts. Secondly, the RGB color model is applied in the teaching of color design, and the beauty of the color scheme is calculated through the Berkhof equation in color harmony so as to improve the color-matching ability of students’ art design. Big data analysis is used to evaluate the students’ design works comprehensively. Finally, taking College H as an example, the effectiveness of computer-aided teaching in graphic arts design is analyzed, and the impact of the method on the employment of graduates is verified. The results show that in the experimental group using computer-assisted teaching and the control group of traditional teaching mode, the average grade difference is 17.91 points, Sig=0.003. The difference is significant, which indicates that computer-assisted teaching significantly improves the students’ art design professional ability. This innovative model has a significant impact on the enhancement of art design majors in colleges and universities and provides a practical path for innovative artistic design teaching.

It has become difficult for conventional higher vocational education to fulfill the needs of the digital era, and reforming the “three teachings” is now a crucial decision to promote the high-quality expansion of higher vocational education. Promoting the high-quality development of higher vocational education by adopting a practical approach to reforming the “Three Teachings” in the age of digitalization. In a composite system model, the sequence parameters for the higher vocational school subsystem are determined by the sequence parameters of the ‘Three Teachings’ reform. The TOPSIS method and Moran’s I were used to assess the rate of growth of China’s higher vocational school system to look into the main factors that have contributeds to the good development of higher vocational education in that nation. The results show that there is weak geographical clustering, with each region’s 2022 Moran’s I for the “Three Teachings” reform and the high-quality development of higher vocational education being just 0.1280. Moreover, Moran’s I show a general downward trend from 2013 to 2022, suggesting that challenges need to be addressed to keep improving higher vocational education in a top-notch way. The ‘Three Teachings’ reform strategy presented in this study may help further sustain the long-term, high-quality expansion of higher vocational education.

The relationship between Chinese art and design aesthetics and traditional Chinese philosophical concepts is inextricably linked. In this paper, the design elements in art design are first decomposed into characteristic elements, and the value domain of aesthetic characteristic parameters is determined by constructing the mathematical model between each characteristic element and sensual imagery. The aesthetic degree judgment method was used to quantify the aesthetic features. To examine the maximum factors that influence the aesthetic degree of art design, a multivariate quantitative model was utilized. Finally, through the quantification of aesthetics, the aesthetic concepts of national traditional culture in art design were analyzed, and the perception of aesthetics was elevated from perceptual to rational level so as to obtain the design art scheme and design law. The results show that the five elements of fire, earth, wood, water and gold colors in Chinese traditional art design, with brightness values of 65, 37, 28, 54 and 21, respectively, conform to the ratio of gray to medium tones, and generally interpret the cultural connotations of softness, harmony, conservatism, heaviness, simplicity, calmness and generosity. In this paper, we gain more knowledge about the traditional aesthetic culture in art design and encourage the application of national traditional culture in art design.

Art design for visual communication is an innovative model that combines art, design and communication, aiming to present artworks through visual elements and design principles. This paper utilizes level 5 features extracted from pillar networks as a rough unimodal feature to extract saliency targets in art design images. Improving their performance can be achieved by cascading different unimodal features. Design the multi-branch group fusion module, which is divided into multi- and single-branch fusion branches according to the number of groups to extract different numbers of image features. Determine the classification of art images by their original and stroke information features. Design a quantitative analysis scheme for image data to analyze the role of art design from the perspective of RGB, HSV, and image visual information communication effect. In the image HSV analysis, the hue distribution region of the sample original image is mainly concentrated in [0,0.1] and [0.5,0.6], the distribution is more concentrated, and 1/3 of the maximum value of the saturation is under 0.8, which shows the visual characteristics mainly through the hue component. A better communication effect can be attributed to the mean value of the color conciseness score reaching 4.212 in the image visual communication effect.

Facing the current severe employment situation, colleges and universities need to strengthen the cultivation and management of talents to meet the social demand. Under the guidance of SWOT theory, this paper combs out the system model of college students’ career planning. And from the four aspects of advantages, disadvantages, opportunities and challenges, it constructs the evaluation index system of college students’ career planning based on SWOT. Combined with the principle of triangular fuzzy number, the evaluation model of college students’ career planning based on fuzzy network analysis is constructed. Based on this, college students in 8 colleges and universities are selected as research objects. After collecting relevant data, the weights of each index are calculated, and the career planning of college students in 8 colleges and universities is comprehensively evaluated and analyzed from the perspectives of different genders and majors. The results show that the indicators of the strengths level have the most significant influence on college students’ career planning, with a weight of 0.357. Only college 3 has a comprehensive score of college students’ career planning greater than 90, male college students (72.76) have a slightly higher complete score of career planning than female college students (71.81), and students of science (76.92) have a somewhat higher comprehensive score of career planning than students of liberal arts (74.68). Combined with the analysis of this paper, college students’ career planning education can be improved by guiding them to establish precise career planning, improving specialized guidance services, and increasing recruitment activities.

This paper focuses on analyzing the critical technologies of artificial intelligence technology in the creation and display of art and design teaching in colleges and universities, respectively, using image processing technology, image geometric transformation, and image special effects processing to realize the basic image design of art design, and using head-eye synergistic eye-movement multi-channel interaction and gesture interaction to form an immersive interactive art and design display device. Combined with the radar chart of the acceptance of AI technology in the course teaching, the influencing factors of the three dimensions of cognition, interest and Practice are explored. The OBE concept is introduced to propose a teaching reform program for art and design courses under AI technology. Practical application of the teaching program can improve students’ visual literacy in art and design, and its numerical difference is 0.3254 at the most. Thus, the teaching reform program of art and design courses in colleges and universities based on the OBE concept proposed in this paper is feasible to a certain extent.

Virtual reality technology has transformed numerous fields, including art design, where its implications are particularly profound. This study explores the integration of optical illusion with virtual reality to offer novel expressions and perspectives in art. This approach generates unparalleled visual effects by leveraging visual discrepancies and augmented reality. It softens the divide between the virtual and tangible worlds, enriching art design’s creative domain.Our research demonstrates the effective visualization of three-dimensional objects and the streamlined post-processing of images. Employing optical illusion technology, we observed an average enhancement of 37% in the accuracy of categorizing illusions, alongside a notable increase in the perceived depth of images without special equipment. Specifically, the perceived three-dimensional effect of images processed through this technology scored an average of 67.57, significantly from the unprocessed images’ score of 54.83. Furthermore, a case study on ‘No Signal’ design illustrates how dynamic optical illusions can dramatically amplify the visual impact and novelty experienced by viewers.In summary, applying optical illusions grounded in virtual reality technology profoundly enhances artworks’ visual appeal and broadens art design’s expressive capabilities. This advancement holds considerable theoretical and practical relevance, underscoring its significance and potential in art and design.

This study ventures into the captivating realm of visual illusion art, where the enigmatic principles of visual perception are harnessed to enhance artistic creativity. Through mathematical modeling of the visual perception process, we uncover the essence of visual illusions and their profound impact on art and design. Leveraging cellular neural networks, this research merges dynamic processes with the WC visual illusion and color space models to craft a novel visual illusion neural network model adept at reproducing the nuances of visual illusion art. Our investigation into the application of visual illusions in art design reveals a notable affinity for the “transparent” quality, achieving a 57% certainty level and embodying the art’s ethereal nature. Furthermore, we identify significant correlations between interactive effects, color coordination, design structure, visual impact, and the overarching quality of art designs, with correlation indices of 0.508, 0.487, 0.535, and 0.602, respectively. This work highlights visual illusion’s pivotal role in propelling the field of art and design forward, thereby enriching the tapestry of human experience.

Art and design professional education can not keep up with the development speed of the industry, and there is no reference to the market demand data, failing to keep pace with the times, which brings many negative impacts on the employment of students. This paper suggests an art design professional education model that incorporates VR interactive scene technology for this reason. Through the double camera, we simulate the visual principle of the human eye, collect three-dimensional data from VR interactive scenes, and use the image coordinate system and pixel coordinate system conversion operations to pre-process the collected data. The optimal layout solution is sought by adjusting the layout to generate a realistic 3D model of the teaching scene and to realize the construction of the VR interactive scene. The principle and implementation process of the art professional education model supported by the VR interactive scene is elaborated, and the experimental comparison method is used to empirically analyze the art and design professional education model integrating VR technology. The results show that there is a significant difference between the experimental group and the control group in the dimensions of adaptability (0.004) and uniqueness (0.044) of design thinking and the cultivation of design thinking ability (0.016) (P

Chinese traditional culture has rich cultural connotations, which has an important role in promoting the development of modern art and design education. The study examines the application of deep learning technology in art and design education through the analysis of several target design colleges and universities based on the background of deep learning. Then, it starts from the creation of art images based on traditional cultural style migration, constructs a style migration model based on a convolutional neural network, and evaluates the effect of style migration images of traditional cultural elements. Create teaching experiments to investigate the utilization of deep learning technology in art and design education. At present, teachers and students still need to improve their cognition of the use of deep learning technology in art design, and the use of deep learning technology in art design has been popularized to a certain extent, with computer vision technology (90.7%) and natural language understanding technology (71.2%) being the most used. The images generated by the style migration model integrating traditional cultural elements have high PSNR values (12~20) and SSIM values (0.375~0.633), and the performance of the students using the model is 6.98% higher than that of the control students, which reflects the validity and feasibility of the application of the model in the creation of art design. It is necessary to optimize the curriculum system, enrich the teaching mode, cultivate the appreciation ability, carry out practical activities and create a good atmosphere and other dimensions to promote the talent cultivation of traditional culture in art design.

Computer vision, as a technical means of “seeing” the world, is applied to modern art design, which can expand the audience’s perception of external reality and re-understand the process of art design. This paper examines the process evolution and creation method of generative art design by analyzing the characteristics of computer vision technology and visual communication design. Based on a capsule network and a generative adversarial network, the C-CapsGAN model for art design image generation has been established. Optimization functions for consistency, style loss, and smoothness loss have been designed. Quantitative analysis and survey research were used to analyze the effectiveness of the C-CapsGAN model in art design image generation. It was found that after the model convergence, the FID value was reduced by 20.36%, and the IS value was improved by 0.482 compared to CCME-GAN on the modern art design image dataset. The mean SSIM value of the six art design images was 0.611, and the subject’s favorite score for the model-generated art design images could be up to 3.715 points. By integrating computer vision technology with modern art design, the diversification of art images can be promoted, and the visual stimulation of art design can be enhanced for the audience.

The integration of art design and ideological and political education in colleges and universities is an inevitable choice for the establishment of moral education, and it is an important part of the systematic project of “educating people”. Based on the relevant countermeasures for integrating ideological and political education into art design courses, this paper proposes an adaptive network teaching platform. The platform constructs a feedback mechanism and recommendation mechanism based on the relationship between students’ ability value and the difficulty of teaching resources and adjusts the difficulty of learning materials by the method of cooperative voting to complete the adaptive guidance. The experimental results show that the experimental group’s performance in all dimensions of Civics is 0.3766 points higher than the control group on average, and the performance of the three art design courses of the students who are taught using this platform is improved by 11.32, 11.91 and 11.92 points on average respectively, and the average number of classroom answers before and after the experiment is improved by 4.35 times. It shows that the integrated teaching of art design and ideological and political education using the network teaching platform proposed in this paper helps to improve students’ art design and ideological and political academic level and is also conducive to the enhancement of learning interest.

Under the implementation of new talent cultivation goals, the gaps in art design teaching are gradually revealed, and various teaching shortcomings have become stumbling blocks on the road of professional education development, which need to be improved in time. Combined with information technology, this paper proposes three curriculum integration strategies: enhancing audio-visual sensory effects, applying microteaching, and expanding the sharing channels of the art design resource library. A teaching resource application platform has been constructed for the art and design resource sharing strategy, which combines user-based and project-based collaborative filtering algorithms and is applied to the resource recommendation module. The performance of the system is evaluated through algorithmic comparison, and a satisfaction survey is conducted on the three dimensions of perceived usefulness, perceived ease of use, and intention to use for art and design students who use the system. The average satisfaction values in the three dimensions were 76.99%, 78.35%, and 79.5%, respectively, indicating that the art design students were more satisfied with the teaching resource application platform designed in this paper.

Data visualization has become a popular discipline in today’s society, and understanding this trend has become a hot topic in the field of design. This paper introduces the visual perception system of the human eye, combining graphic features and dynamic features to reveal the visualization mechanism of information dynamic conveying in the field of art design. Then, the quantitative model of visual communication in color and graphic texture in art design, respectively, and the art design image containing rich plants are taken as an example to realize the visual analysis of information communication in the field of art design. There is a significant negative correlation (p > 0.05) between the purity of the foreground image, the foreground image color gamut area, and the proportion of foreground image color matching. This means that the higher the purity of the foreground image, the smaller the color matching area, leading to more coordinated color matching and a higher aesthetic degree. Simultaneously, the alteration in the gray value of the image texture typically exhibits characteristics such as high frequency, small amplitude, and randomness. The use of visualization technology in art design, reasonable collocation of colors, and graphic aesthetics fully demonstrate its aesthetic value.

Art designs exhibit different principles, textures, color combinations, and creative skills for vivid thinking visualizations. Art exhibits are far from ages, periods, and creators finding their digital patterns in recent years for resurrection. Degraded periodic artworks are digitally handled for reviving their legacy using digital image processing. This article introduces Textural Restoration Technique (TRT) using Deep Feature Processing (DFP) to augment such innovations. The proposed technique analyses the tampered image for its textures, and available features are extracted. The textures are expected to be sequential based on gradient distribution; the missing gradients are identified from the available features near the region of interest (ROI). The ROI is marked by combining missing and available features from which textural edges are sketched. In this process, recurrent learning is employed for verifying the gradient substitutions for even textures. The texture patterns are classified using high and low accuracy features exhibited between two successive ROIs. First, the learning model is trained using gradient distribution accuracy pursued by the texture completion edge. The second training is pursued by the first distribution, achieving the maximum restoration. The filled features and their gradient positions are marked by moving the ROIs for distinguishing textures. The restoration ratio is computed with high accuracy based on the filled edges.

Under the background of the continuous impact of new media, with the deepening of art education reform, more creative teaching thinking and teaching methods are integrated into the art design education classroom in universities. In the continuous progress of art design major, how to change the traditional art education classroom and make the teaching more innovative has become one of the research contents that the teaching workers focus on. Therefore, in order to export interdisciplinary talents to the society, it is an inevitable trend for universities to act interdisciplinary teaching and learning mode in combination with the background of new engineering and the widely used STEAM concept. This paper analyzes the teaching mode of art design major in higher education, and then analyzes the current situation of engineering practice courses in universities. According to the characteristics of art students and engineering students, it explores the interdisciplinary teaching mode of introducing art design elements into engineering practice courses, which can provide the orderly development of art education with reference in universities. [ABSTRACT FROM AUTHOR]

The rapid advancement of industrialization has sparked the emergence of diverse art and design theories. As a trailblazer in the realm of industrial art and design theory, visual communication has transcended the boundaries of merely arranging and combining individual elements. Embracing the potential of artificial intelligence technology, the extraction of multidimensional abstract data and the acceleration of the art design process have gained considerable momentum. This study delves into the abstract emotional facets within the methodology of visual communication art design. Initially, convolutional neural networks (CNN) are employed to extract expressive features from the poster's visual information. Subsequently, these features are utilized to cluster emotional elements using a variational autoencoder (VAE). Through this clustering process, the poster images are categorized into positive, negative, and neutral classes. Experimental results demonstrate a silhouette coefficient surpassing 0.7, while the system framework exhibits clustering accuracy and efficiency exceeding 80% in single sentiment class testing. These outcomes underscore the efficacy of the proposed CNN-VAE-based clustering framework in analyzing the dynamic content of design elements. This framework presents a novel approach for future art design within the realm of visual communication. [ABSTRACT FROM AUTHOR]

The research expects to promote "artificial intelligence (AI) + Education" and cultivate high-quality art design talents with international perspectives. First, random sampling recruits 100 Art Design sophomores in a university and divides them into a test group and a control group. The Questionnaire Survey (QS) is used to obtain research data, including students' satisfaction with the curriculum, training mode, and innovation environment. Meanwhile, some deficiencies in the traditional educational model are revealed alongside targeted suggestions. Then, a comparative analysis is performed on the design works of the two groups and the student's evaluation of the existing and proposed models. The result proves the effectiveness of the proposed international art design talents-oriented Human–Computer Interaction (HCI)-based training model. The research results imply that students are less satisfied with the existing curriculum, training mode, and innovative environment. About 40% of students hold a neutral attitude towards the current curriculum. They hope that the school provides a more comprehensive and flexible curriculum, personalized training methods, and a relaxed learning environment conducive to creativity. Students in the test and the control groups have gained significant differences in the scores of design works. The test group has scored relatively high, and their works contain more international elements than the control group. Meanwhile, the test group's works reflect a deeper understanding of theoretical knowledge. They give a high evaluation of the proposed talent training model. Thus, the proposed HCI-based new talent training model is effective. Therefore, the proposal is of great significance for promoting the "AI + Education" and talent training models. [ABSTRACT FROM AUTHOR]