Referenceless Quality Evaluation of Tone-Mapped HDR and Multiexposure Fused Images

Abstract

Nowadays, the standard dynamic range (SDR) image acquired at a fixed exposure exposes weakness in portraying fine-grained details of real scenes. The high dynamic range (HDR) image and other types of SDR images generated by multiexposure fusion techniques provide us new choices for scene representation. To display on SDR screens, an HDR image must be tone-mapped to an SDR one. Since different tone-mapping/fusion algorithms produce images with varying visual quality levels, it naturally desires a quality evaluation model for comparison. This article proposes an effective model in the absence of the reference image. By analyzing the characteristics of tone-mapped HDR and multiexposure fused images, we first extract multiple quality-sensitive features from the following aspects: 1) colorfulness; 2) exposure; and 3) naturalness. Then, the model is built by bridging all extracted features and associated subjective ratings via support vector regression. Extensive experiments on publicly available databases prove the superiority of our model over the state-of-the-art referenceless quality evaluation ones.