Unsupervised feature learning framework for no-reference image quality assessment

Peng Ye, J. Kumar, Le Kang, D. Doermann
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引用次数: 710

Abstract

In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to build a computational model to automatically predict human perceived image quality without a reference image and without knowing the distortion present in the image. Previous approaches for this problem typically rely on hand-crafted features which are carefully designed based on prior knowledge. In contrast, we use raw-image-patches extracted from a set of unlabeled images to learn a dictionary in an unsupervised manner. We use soft-assignment coding with max pooling to obtain effective image representations for quality estimation. The proposed algorithm is very computationally appealing, using raw image patches as local descriptors and using soft-assignment for encoding. Furthermore, unlike previous methods, our unsupervised feature learning strategy enables our method to adapt to different domains. CORNIA (Codebook Representation for No-Reference Image Assessment) is tested on LIVE database and shown to perform statistically better than the full-reference quality measure, structural similarity index (SSIM) and is shown to be comparable to state-of-the-art general purpose NR-IQA algorithms.
无参考图像质量评估的无监督特征学习框架
本文提出了一种基于无监督特征学习的通用目标无参考图像质量评估框架。目标是建立一个计算模型,在没有参考图像和不知道图像中存在失真的情况下自动预测人类感知的图像质量。以前解决这个问题的方法通常依赖于基于先验知识精心设计的手工特征。相反,我们使用从一组未标记的图像中提取的原始图像补丁以无监督的方式学习字典。我们使用带有最大池化的软分配编码来获得用于质量估计的有效图像表示。该算法使用原始图像补丁作为局部描述符,并使用软分配进行编码,在计算上非常吸引人。此外,与以前的方法不同,我们的无监督特征学习策略使我们的方法能够适应不同的领域。CORNIA(无参考图像评估的码本表示)在LIVE数据库上进行了测试,结果显示其统计性能优于全参考质量度量,结构相似性指数(SSIM),并与最先进的通用NR-IQA算法相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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