  Tone mapping operators and multi-exposure fusion methods allow us to enjoy
the informative contents of high dynamic range (HDR) images with standard
dynamic range devices, but also introduce distortions into HDR contents.
Therefore methods are needed to evaluate tone-mapped image quality. Due to the
complexity of possible distortions in a tone-mapped image, information from
different scales and different levels should be considered when predicting
tone-mapped image quality. So we propose a new no-reference method of
tone-mapped image quality assessment based on multi-scale and multi-layer
features that are extracted from a pre-trained deep convolutional neural
network model. After being aggregated, the extracted features are mapped to
quality predictions by regression. The proposed method is tested on the largest
public database for TMIQA and compared to existing no-reference methods. The
experimental results show that the proposed method achieves better performance.
