Lanterna Rally

Author: Omar Abdulrahman Alaql

Publisher:

Published: 2017

Total Pages: 0

ISBN-13:

The last decade has witnessed great advances in digital images. Massive numbers of digital images are being captured by mobile digital cameras due to the increasing popularity of mobile imaging devices. These images are subjected to many processing stages during storing, transmitting, or sharing over a network connection. Unfortunately, these processing stages could potentially add visual degradation to original image. These degradations reduce the perceived visual quality which leads to an unsatisfactory experience for human viewers. Therefore, Image Quality Assessment (IQA) has become a topic of high interest and intense research over the last decade. The aim of IQA is to automatically assess image quality in agreement with human judgments.This dissertation mainly focuses on the most challenging category of IQA - general- purpose No-Reference Image Quality Assessment (NR-IQA), where the goal is to assess the quality of images without information about the reference images and without prior knowledge about the types of distortions in the tested image. This dissertation contributes to the research of image quality assessment by proposing three novel approaches for NR- IQA and one model for image distortions classification. First, we propose improvements in image distortions classification by introducing a training model based on new features collection. Second, we propose a NR-IQA technique, which utilizes our improvement in the classification model, and based on a hypothesis that an effective combination of image features can be used to develop efficient NR-IQA approaches. Third, a NR-IQA technique is proposed based on Natural Scene Statistics (NSS) by finding the distance between the natural images and the distorted images in 3D dimensional space. Forth, a novel NR-IQA approach is presented, by utilizing multiple Deep Belief Networks (DBNs) with multiple regression models. We have evaluated the performance of the proposed and some existing models on a fair basis. The obtained results show that our models give better results and yield a significant improvement.