Enhancing Visual Media Engagement: Real-Time Cartoonization and Feature Enhancement of Photos and Videos

Registration ID: IJNRD_224538

Published ID: IJNRD2407030

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RAGANNAGARI VENKATA SREEVIDYA, P Satish Kumar, Yeddula sreelatha

Visual Media Engagement, Real-Time Cartoonization, Feature Enhancement of Photos and Videos

The demand for transforming real-life photographs and videos into stylized cartoon versions has surged with the rise of social media and digital content creation. This project proposes an innovative approach using Generative Adversarial Networks (GANs) to achieve real-time cartoonization, aimed at enhancing user engagement and creativity across digital platforms. The project leverages advanced machine learning techniques implemented through the Python OpenCV package. Initially, the methodology involves preprocessing steps to optimize input media quality. This includes upsampling to enhance resolution and denoising to reduce visual artifacts, ensuring high-quality transformations. Subsequently, GAN-based models are employed for cartoonization, trained on a diverse dataset encompassing various artistic styles and preferences. The models generate cartoonized versions of photographs and videos with real-time processing capabilities, catering to dynamic digital content needs. Beyond cartoonization, the project integrates feature enhancement functionalities. Filters are applied to stylize images according to user preferences, enhancing visual appeal and artistic expression. This iterative process allows users to customize and refine cartoonized outputs, fostering creativity and personalization in digital content creation. Moreover, the project includes tools for converting cartoonized videos into GIFs, facilitating easy sharing and dissemination on social media platforms. Central to the project is the development of a user-friendly image hub. This interface serves as a centralized platform where users can upload, process, and download their transformed media seamlessly. Intuitive controls enable users to adjust cartoonization parameters, explore different styles, and preview results in real-time. The interface design prioritizes accessibility and usability, accommodating both casual users and digital content creators seeking innovative ways to engage audiences. The proposed method holds broad applications across creative industries such as comic book production, anime creation, and digital storytelling. By democratizing the process of cartoonization and feature enhancement, the project empowers users to explore new avenues of visual expression and storytelling. It bridges traditional media with digital innovation, offering versatile tools to amplify visual content impact and audience engagement in the digital era. In conclusion, this project represents a significant advancement in real-time cartoonization and feature enhancement of photographs and videos using GANs and image processing techniques. By combining technical sophistication with user-centric design, the project aims to redefine digital media engagement and creativity. Through accessible tools and customizable functionalities, it seeks to inspire and empower users to unleash their artistic potential, reshaping digital storytelling and visual communication landscapes.

Published Paper   E-Certificate

RAGANNAGARI VENKATA SREEVIDYA, P Satish Kumar, & Yeddula sreelatha (July-2024). Enhancing Visual Media Engagement: Real-Time Cartoonization and Feature Enhancement of Photos and Videos. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), a287-a296. https://ijnrd.org/papers/IJNRD2407030.pdf

Volume 9 Issue 7, July-2024

Pages : a287-a296

Paper Reg. ID: IJNRD_224538

Published Paper Id: IJNRD2407030

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Cuddapah, Andra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407030.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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