Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment

by   Jun Fu, et al.

With the emergence of image super-resolution (SR) algorithm, how to blindly evaluate the quality of super-resolution images has become an urgent task. However, existing blind SR image quality assessment (IQA) metrics merely focus on visual characteristics of super-resolution images, ignoring the available scale information. In this paper, we reveal that the scale factor has a statistically significant impact on subjective quality scores of SR images, indicating that the scale information can be used to guide the task of blind SR IQA. Motivated by this, we propose a scale guided hypernetwork framework that evaluates SR image quality in a scale-adaptive manner. Specifically, the blind SR IQA procedure is divided into three stages, i.e., content perception, evaluation rule generation, and quality prediction. After content perception, a hypernetwork generates the evaluation rule used in quality prediction based on the scale factor of the SR image. We apply the proposed scale guided hypernetwork framework to existing representative blind IQA metrics, and experimental results show that the proposed framework not only boosts the performance of these IQA metrics but also enhances their generalization abilities. Source code will be available at


page 1

page 6


Textural-Structural Joint Learning for No-Reference Super-Resolution Image Quality Assessment

Image super-resolution (SR) has been widely investigated in recent years...

Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation

Despite several solutions and experiments have been conducted recently a...

Learned Image Downscaling for Upscaling using Content Adaptive Resampler

Deep convolutional neural network based image super-resolution (SR) mode...

SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution

The image Super-Resolution (SR) technique has greatly improved the visua...

Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity

There has been a growing interest in developing image super-resolution (...

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

Latest advances in Super-Resolution (SR) have been tested with general p...

Please sign up or login with your details

Forgot password? Click here to reset