A Deep Learning Framework for Blended Distortion Segmentation in Stitched Images

Abstract

The visual quality of stitched images plays an important role to provide the high-quality immersive viewing experience of Virtual Reality (VR) contents. There are several image stitching algorithms that generate panoramas from multiple images taken with different cameras and angle of views. The performance of these stitching algorithms can be measured by estimating the quality of generated panoramas. This paper presents a segmentation-based Stitched Image Quality Assessment (SIQA) approach that captures the blended distortion in stitched images and segments the distorted region using binary mask. The segmented regions provide the location and total area of the distorted region. The results obtained from the experimental evaluation validate the reliability of our method for capturing the blended distortions in stitched images.