HDV

Degree

Bachelor of Science (Computer Science)

Department

Department of Computer Science

School

School of Mathematics and Computer Science (SMCS)

Advisor

Dr. Muhammad Saeed, Visiting Faculty, Department of Computer Science

Co-Advisor

Umair Ahmed, Senior Software Development Engineer, Remotebase

Keywords

GAN, Frame Interpolation, Upscaling, Deep Learning, Super Resolution, Video Enhancement

Abstract

HDV aims to enhance low-quality videos into higher resolution and quality content. Existing solutions often require professional expertise or offer limited features, making them unsuitable for amateurs or those seeking comprehensive solutions. The primary objective is to provide a one-stop, intelligent video processing solution to improve footage with poor production value, allowing users to enhance digital content without complex settings. The methodology involves exploring multiple avenues to balance quality and performance, employing a layered architecture and deep learning models for key enhancements such as upscaling, sharpening, noise reduction, and frame interpolation. The system processes files through a validation, enhancement, and export pipeline managed by dedicated subsystems.

Tools and Technologies Used

Python, Electron.js, Pytorch, Real-ESRGAN, SRCNN, SRGAN, FSRCNN, EMA-VFI, OpenCV

Methodology

The project began with requirements gathering to identify the need for a smart, all-in-one video processing solution aimed at enhancing low-quality footage through upscaling, sharpening, noise reduction, and frame interpolation. Functional and non-functional requirements were defined to ensure usability and performance. Based on these, a modular, scalable, and maintainable system architecture was designed using a layered approach—comprising Presentation, Application, and Processing Layers—and incorporating key subsystems like FileHandler, EnhancementEngine, and ExportManager. A functional prototype was then developed using Gradio to demonstrate core features, with iterative refinement across validation, enhancement, and export stages.

Document Type

Restricted Access

Submission Type

BSCS Final Year Project

Download

Share

COinS