Introduction

This is the research code for the CVIU 2017 paper:

Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang, " Learning a No-Reference Quality Metric for Single-Image Super-Rolution", CVIU 2017.

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Abstract

Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception. While most super-resolution images are evaluated by full-reference metrics, the effectiveness is not clear, and the required ground-truth images are not always available in practice. To address these problems, we conduct human subject studies using a large set of super-resolution images and propose a no-reference metric learned from visual perceptual scores. Specifically, we design three types of low-level statistical features in both spatial and frequency domains to quantify super-resolved artifacts, and learn a two-stage regression model to predict the quality scores of super-resolution images without referring ground-truth images. Extensive experimental results show that the proposed metric is effective and efficient to assess the quality of super-resolution images based on human perception.

Quick Start

run "demo.m"

Citation

If you find the code and dataset useful in your research, please consider citing:

@article{Ma-Metric-2017,
    title={Learning a No-Reference Quality Metric for Single-Image Super-Rolution},
    Author = {Ma, Chao and Yang, Chih-Yuan and Yang, Xiaokang and Yang, Ming-Hsuan},
    journal = {Computer Vision and Image Understanding},
    pages={1-16},
    Year = {2017}
}