Optimization-Based Tone Mapping Evaluation

Abstract

AbstractIn recent years, increasing attention has been paid to devising tone mapping operators, which convert specialized high dynamic range (HDR) images to standard low dynamic range (LDR) ones for visualization on daily monitors. However, there lacks a reliable evaluation criterion for comparing distinct tone mapping operators, which is of great significance to the design and optimization of tone mapping methods. In this paper we propose an effective tone mapping evaluation system (TMES) based on a two-stage framework. In the first stage, features are extracted in view of the observations that luminance information, color saturation, statistical naturalness, structural fidelity and visual saliency have different and determinate influences on the perceptual quality of tone-mapped LDR images. In the second stage, the extracted features are integrated with a data-driven optimization strategy, which iteratively learns the parameters by applying thousands of collected tone-mapped LDR and natural images. Our TMES evaluation system can be implemented with or without reference HDR images, serving for the optimization and monitoring of tone mapping methods. Experiments conducted on three databases prove the superiority of our quality evaluation system.