# FSITM: A Feature Similarity Index For Tone-Mapped Images

## TL;DR

This paper introduces FSITM, a new objective index based on local phase information to evaluate tone-mapped images, outperforming existing metrics and enhancing evaluation accuracy when combined with TMQI.

## Contribution

The paper proposes FSITM, a novel feature similarity index for tone-mapped images based on local phase, improving evaluation performance over existing metrics.

## Key findings

- FSITM outperforms TMQI in experiments.
- Combining FSITM with TMQI yields higher evaluation accuracy.
- The MATLAB implementation is publicly available.

## Abstract

In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices. The MATLAB source code of the proposed metric(s) is available at https://www.mathworks.com/matlabcentral/fileexchange/59814.