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[Defense] Image Quality for Object Detection in Compressed Videos

Friday, July 15, 2022

11:00 am - 12:00 pm

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Poonam Beniwal
will defend her dissertation
Image Quality for Object Detection in Compressed Videos


Abstract

The amount of video data generated daily is enormous, making it nearly impossible for humans to understand the content of the data. The use of machine learning and deep learning approaches for automatic analysis has grown. It is crucial to determine the robustness and reliability of the automated analysis. The reliability of automated systems can be evaluated using a variety of factors. One such parameter is compression, which is an inherent part in video transmission and storage. We analyzed the impact of compression on three computer vision algorithms. The dataset used for analysis is collected from an IP-based surveillance camera and compressed using different bandwidths and quantization levels. We also find a correlation between image quality and the performance of algorithms. Existing image quality metrics cannot explain the drop in performance for object detection. The traditional image quality metrics define quality from a human perspective. We introduced full-reference and no-reference metrics to overcome the constraints of existing image quality metrics. We present the performance of the image quality metric on different aspects of object detection.


Friday, July 15, 2022
11:00AM - 12:00PM CT
Online via MS Teams

Dr. Shishir Shah, dissertation advisor

Faculty, students and the general public are invited.

Doctoral Dissertation Defense