Investigation of Different Aspects of Image Quality

Image quality is a fundamental problem in computer vision. For variety of applications, for instance scanning documents, QR codes, bar codes or algorithms like object detection, recognition, tracking, scene understanding etc. images with good contrast, high illumination and sharpness are desired. Similarly in computer graphics for information visualisation, animations, presentations etc. aesthetically pleasing design and good colorization of the images are desired. Therefore the definition of image quality depend on the context and application of the image. In this thesis we attempt to address various challenges pertaining to image quality, (1) for natural imaging, we explore a novel approach for predicting the capture quality of the images taken in the wild. (2) For Graphics designs, we explore the aesthetic quality of images by suggesting multiple aesthetically pleasing colorization of graphics designs. Due to increasing advancements and portability of smartphone cameras, it has become a default choice for capturing images in the wild. However, there are quality issues with camera captured images due to reasons like lack of stability during capture process. This hinders the automatic workflows which takes camera captured images as input e.g. Optical Character Recognition (OCR) for documents image, face detection/recognition from human image etc. Part of this thesis is focused on Image Quality Assessment (IQA), the aim is to quantify the degradation like out-of-focus blur and motion artefacts in a given image. One of the major challenge in IQA for images captured in the wild is that, we do not have ground truth to measure the capture quality. Therefore various previous attempts of IQA require human in loop for creating the ground truths for capture quality of images. Large user studies are conducted and mean human opinion scores are then used as measure for quality. In this work we use a signal processing based technique to generate the IQA ground truth, and propose a comprehensive IQA dataset which is a good representative of the real degradation during the process of capture. Further, we propose deep learning based approach to predict image quality for captures in the wild. Such IQA algorithm can be helpful in the cause by either giving online quality suggestion during capture or rating the quality post capture. Another dimension to the image quality is aesthetic quality. Increasing usage of internet, social media and advancement in the mobile camera, photography has become a very popular hobby and interest to a large section. Even for a well captured image, people use varieties of filters and effects post capture to enhance the appearance of the image e.g. adjusting color temperature, contrast or even blurring part of