Statistical Model for the Quality of Panoramic Images of 2D artifacts

Abstract

The field of digital imaging emphasizes the quality of 2D artifact images, often facing challenges when capturing large images due to their wide field of view. A successful technique for addressing this is panoramic image creation, which involves merging overlapping segments from a larger image. Research in this domain focuses on understanding the visual quality aspects of panoramic images. This study aims to achieve two main objectives: firstly, to identify the key visual quality attributes associated with panoramic images, and secondly, to propose predictor variables for a statistical model that assesses the quality of 2D artifact panoramic images. To accomplish this, the researchers conducted a case study centered on generating panoramic images of mural paintings found in Sri Lankan temples. Through their investigation, they pinpointed color balance and noise & distortion as the most significant factors influencing the overall quality of these images. The researchers employed three methods to create the panoramas: an innovative technique, alongside two established methods—Photoshop and Hugin. Experts in Visual Arts evaluated the resulting images using a four-point Likert scale. Color balance and noise & distortion were used as predictor variables, while overall quality was the response variable. The gathered data underwent analysis using ordinal logistic regression within the Minitab statistical package. The outcomes underscored the pivotal roles of color balance and noise & distortion in determining the quality of panoramic images. Moreover, the findings showcased the model’s high accuracy in fitting the data, reinforcing its effectiveness in assessing panoramic image quality.