PROJECT TITLE :

Detecting and Mapping Video Impairments

ABSTRACT:

Video artefact detection without the benefit of an original reference video is a challenging endeavour. An efficient and innovative dual-path (parallel) excitatory/inhibitory neural network that uses a simple discriminating rule to define a bank of accurate distortion detectors is presented in this paper. Pre-processing each video with a statistical image model makes the learning engine more sensitive to distortion. For visualisation, the complete system is capable of producing full-resolution space-time distortion maps that represent the state of the art in performance. For the first time ever, we have created a full resolution map of artefact detection probabilities using our video impairment mapper (VIDMAP). Eight of the most critical artefact categories found during streaming video source inspection can be accurately detected and mapped by this system's present implementation, including aliasing, video encoding corruptions and quantization, contours/banding and combing. On the whole-image artefact identification challenge, we demonstrate that it is either competitive with or significantly surpasses the previous state of the art. VIDMAP has been trained to detect and map these artefacts and is accessible for public usage and assessment at the following URL: http://live.ece.utexas.edu/research/quality/VIDMAP release.zip.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Securing Real-Time Video Surveillance Data in Vehicular Cloud Computing: A Survey ABSTRACT: The concept of vehicular ad hoc networks, or VANETs, has attracted a lot of attention recently, particularly in the
PROJECT TITLE : Automatic Video Analysis Framework for Exposure Region Recognition in X-Ray Imaging Automation ABSTRACT: The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation
PROJECT TITLE : Accurate and Robust Video Saliency Detection via Self-Paced Diffusion ABSTRACT: In order to estimate video saliency in the short term, traditional video saliency detection algorithms usually follow the common
PROJECT TITLE : Video Behavior Profiling for Anomaly Detection ABSTRACT: The goal of this study is to solve the challenge of modeling video behavior acquired in surveillance cameras for online normal behavior recognition and anomaly
PROJECT TITLE : A Convex Optimization Framework for Video Quality and Resolution Enhancement From Multiple Descriptions ABSTRACT: Streaming and compressing methods Of the last decade, technological advancements have led to a migration

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry