Domain-Aware Adaptive Logarithmic Transformation

Tone mapping (TM) aims to display high dynamic range scenes on media with limited visual information reproduction. Logarithmic transformation is a widely used preprocessing method in TM algorithms. However, the conventional logarithmic transformation does not take the difference in image properties into account, nor does it consider tone mapping algorithms, which are designed based on the luminance or gradient-domain features. There will be problems such as oversaturation and loss of details. Based on the analysis of existing preprocessing methods, this paper proposes a domain-aware adaptive logarithmic transformation AdaLogT as a preprocessing method for TM algorithms. We introduce the parameter p and construct different objective functions for different domains TM algorithms to determine the optimal parameter values adaptively. Specifically, for luminance-domain algorithms, we use image exposure and histogram features to construct objective function; while for gradient-domain algorithms, we introduce texture-aware exponential mean local variance (EMLV) to build objective function. Finally, we propose a joint domain-aware logarithmic preprocessing method for deep-neural-network-based TM algorithms. The experimental results show that the novel preprocessing method AdaLogT endows each domain algorithm with wider scene adaptability and improves the performance in terms of visual effects and objective evaluations, the subjective and objective index scores of the tone mapping quality index improved by 6.04% and 5.90% on average for the algorithms.