A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images

High dynamic range (HDR) imaging enables capturing a wide range of luminance levels existing in real-world scenes. While HDR capturing devices become widespread in the market, the display technology is yet limited in representing full luminance ranges and standard low dynamic range (LDR) displays are currently more prevalent. To visualize the HDR content on traditional displays, tone mapping (TM) operators are introduced that convert HDR content into LDR. The dynamic range compression and different processing steps during TM can lead to loss of scene details, as well as luminance and chrominance changes. Such signal deviations will affect image naturalness and consequently disturb the visual quality of experience. Therefore, research into objective methods for quality evaluation of tone-mapped images has received attention in recent years. In this paper, we proposed a completely blind image quality evaluator for tone-mapped images based on a multi-attribute feature extraction scheme. Due to the diversity of TM distortions, various image characteristics are taken into account to develop an effective metric. The features are designed by considering spectral and spatial entropy, detection probability of visual information, image exposure, sharpness, and color properties. The quality-relevant features are then fed into a machine-learning regression framework to pool a quality score. The validation tests on two benchmark datasets reveal the superior performance of the proposed approach compared to the competing metrics.