A comprehensive approach for image quality assessment using quality-centric embedding and ranking networks
Zeeshan Ali Haider, Sareer Ul Amin, Muhammad Fayaz, Fida Muhammad Khan, Hyeonjoon Moon, Sanghyun Seo
Abstract
• Novel QCERN framework clusters and ranks image quality effectively. • Incorporates Order, Metric, and Center Loss for precise alignment. • Demonstrates superior generalization across diverse datasets. • Offers applications in photography, medical imaging, and surveillance. • Utilizes dynamic score anchors for improved accuracy and adaptability. This paper presents a new technology that focuses on blind image quality assessment (BIQA) through a framework known as Quality-Centric Embedding and Ranking Network (QCERN). The framework ensures maximum efficiency when processing images under various possible scenarios. QCERN is entirely different from contemporary BIQA techniques, which focus solely on regressing quality scores without structured embeddings. In contrast, the proposed model features a well-defined embedding space as its principal focus, in which picture quality is both clustered and ordered. This dynamic quality of images enables QCERN to utilize several adaptive ranking transformers along a geometric space populated by dynamic score anchors representing images of equivalent quality QCERN features a distinct advantage since unlabeled images of interest can be placed by evaluation of their distance to these specified score anchors inductively in the embedding space, improving accuracy as well as generalization across disparate datasets. Multiple loss functions are utilized in this instance, including order and metric loss, to ensure that images are positioned correctly according to their quality while maintaining distinct divisions of quality. With the application of QCERN, numerous experiments have demonstrated its ability to outperform existing models by consistently delivering high-quality predictions across various datasets, making it a competitive option. This quality-centric embedding and ranking methodology is excellent for reliable quality assessment applications, such as in photography, medical imaging, and surveillance.