Can I read SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution on EtoBox?

SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution by Zhang, Keke; Zhao, Tiesong; Chen, Weiling; Niu, Yuzhen; Hu, Jinsong is a scholarly article available to read on EtoBox.

What is SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution about?

The image Super-Resolution (SR) technique has greatly improved the visual quality of images by enhancing their resolutions. It also calls for an efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or their generated images. In this paper, we focus on the SR-IQA under deep learning and propose a Structure-and-Perception-based Quality Evaluation (SPQE). In emerging deep-learning-based SR, a generated high-quality, visually pleasing image may have different structures from its corresponding low-quality image. In such case, how to balance the quality scores between no-reference perceptual quality and referenced structural similarity is a critical issue. To help ease this problem, we give a theoretical analysis on this tradeoff and further calculate adaptive weights for the two types of quality scores. We also propose two deep-learning-based regressors to model the no-reference and referenced scores. By combining the quality scores and their weights, we propose a unified SPQE metric for SR-IQA. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts in different datasets.

Author

Zhang, Keke; Zhao, Tiesong; Chen, Weiling; Niu, Yuzhen; Hu, Jinsong

Published

2022

Language

EN