Sensors, Vol. 20, Pages 6457: DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors, Vol. 20, Pages 6457: DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment Sensors doi: 10.3390/s20226457 Authors: Hayat Ullah Muhammad Irfan Kyungjin Han Jong Weon Lee Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and loc...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research