• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.160-165
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• 2023

The pixel art style evolved from the shortcomings of computer image display technology has gradually transformed from a technical limitation into a widely recognized form of artistic expression since development in the early 20th century. This study analyzes the application and characteristics of the expandability of pixel art style design content in sub industries such as physical goods, environmental design, website design, digital art and illustration in the design field. It aims to explore the visual expression and sustainable development form of pixel art style under the development of new media technology that contradicts traditional technological concepts. The research results show that although the pixel art style generally pursues external visual features such as pixelated visual effects, sawtooth and matrix arrangements, its expansion in the art field shows a unique diversity of visual expressions. It has become an important means to convey nostalgic emotions and cultural values. Through this research, we hope to inspire more academic researchers and technology practitioners to explore the development potential of the pixel art style in emerging fields and promote its innovative application in design practice.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.908-909
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• 2023

Recently, several generative models have emerged and are being used in various industries. Among them, Cycle GAN is still used in various fields such as style transfer, medical care and autonomous driving. In this paper, we propose two methods to improve the performance of these Cycle GAN model. The ReLU activation function previously used in the generator was changed to Leaky ReLU. And a new loss function is proposed that considers the semantic level rather than focusing only on the pixel level through the VGG feature extractor. The proposed model showed quality improvement on the test set in the art domain, and it can be expected to be applied to other domains in the future to improve performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2115-2127
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• 2021

Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15~28% and 25~100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model.