ICTACT Journal on Image and Video Processing

B.K. Harsha ¹, B. Srinivasa Rao ², S. Tamijeselvan ³, M. Ganesha ⁴, Nihar Ranjan Behera ⁵

Affiliations
1 School of Computing and Information Technology, Reva University, IN
2 Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, IN
3 Department of Radiography, Mother Theresa PG and Research Institute of Health Sciences, IN
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, IN
5 Department of Business and Management, Swiss School of Business and Management Geneva, CH

Abstract

Image representation and rendering have become critical in numerous applications such as virtual reality, medical imaging, and computer graphics. Traditional rendering techniques often face challenges in efficiently handling complex scenes and achieving photorealistic results while maintaining low computational costs. The problem lies in the high-dimensional nature of image data, leading to slow processing times and reduced scalability. This research presents an AI-enhanced technique called Linear RepRender, which leverages deep learning to transform high-dimensional image representations into simplified linear forms for faster rendering. The proposed method employs a combination of convolutional neural networks (CNNs) and linear regression models to reduce image complexity. Specifically, the CNN extracts low-level and high-level features from the image, while the linear regression step approximates the scene’s core visual elements. This hybrid approach significantly improves rendering speed without sacrificing image quality. Furthermore, the method incorporates a loss function optimized for minimizing discrepancies between the rendered and ground truth images. Experimental results demonstrate that Linear RepRender outperforms traditional rendering algorithms, such as ray tracing and rasterization, in terms of computational efficiency and visual accuracy. On a dataset of complex 3D scenes, the proposed method achieved a 35% reduction in rendering time and a 22% improvement in peak signal-to-noise ratio (PSNR) compared to stateof-the-art methods. Additionally, Linear RepRender was able to handle up to 1.5 million polygons per scene with minimal visual artifacts, making it suitable for real-time applications.

Keywords

AI-Enhanced Rendering, Image Representation, Linear Regression, Convolutional Neural Networks, Real-Time Rendering

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