BLIQUE-TMI: Blind Quality Evaluator for Tone-Mapped Images Based on Local and Global Feature Analyses

High dynamic range (HDR) image, which has a powerful capacity to represent the wide dynamic range of real-world scenes, has been receiving attention from both academic and industrial communities. Although HDR imaging devices have become prevalent, the display devices for HDR images are still limited. To facilitate the visualization of HDR images in standard low dynamic range displays, many different tone mapping operators (TMOs) have been developed. To create a fair comparison of different TMOs, this paper proposes a BLInd QUality Evaluator to blindly predict the quality of Tone-Mapped Images (BLIQUE-TMI) without accessing the corresponding HDR versions. BLIQUE-TMI measures the quality of TMIs by considering the following aspects: 1) visual information; 2) local structure; and 3) naturalness. To be specific, quality-aware features related to the former two aspects are extracted in a local manner based on sparse representation, while quality-aware features related to the third aspect are derived based on global statistics modeling in both intensity and color domains. All the extracted local and global quality-aware features constitute a final feature vector. An emergent machine learning technique, i.e., extreme learning machine, is adopted to learn a quality predictor from feature space to quality space. The superiority of BLIQUE-TMI to several leading blind IQA metrics is well demonstrated on two benchmark databases.

I. Introduction

Being able to automatically evaluate image quality, as perceived by human beings, plays an important role in many image processing and computer vision applications. This naturally raises the demand for objective image quality assessment (IQA) metrics. While the interests in objective IQA have been growing at an accelerated pace over the past decades and the latest progress in this field is encouraging, there also has been an abundant menu of unresolved problems left for further study [1]. Among the unresolved problems, quality analysis of tone-mapped images (TMIs) is one of the most challenging problems while the effort made to this area remains quite limited.