Perceptual Tone Mapping Model for High Dynamic Range Imaging

TL;DR

This paper introduces TMOz, a perceptual tone mapping model for HDR images that uses CIECAM16 attributes to improve colorfulness and brightness reproduction, outperforming existing methods.

Contribution

The paper presents a novel automatic and adaptive tone mapping operator based on CIECAM16, enhancing perceptual quality in HDR to SDR conversion.

Findings

TMOz outperforms state-of-the-art TMOs in objective and subjective evaluations.

The model effectively preserves perceptual brightness, colorfulness, and overall image quality.

Psychophysical experiments validated the automatic parameter adjustment.

Abstract

One of the key challenges in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Traditional tone mapping operators (TMOs) compress the luminance of HDR images without considering the surround and display conditions emanating into suboptimal results. Current research addresses this challenge by incorporating perceptual color appearance attributes. In this work, we propose a TMO (TMOz) that leverages CIECAM16 perceptual attributes, i.e., brightness, colorfulness, and hue. TMOz accounts for the effects of both the surround and the display conditions to achieve more optimal colorfulness reproduction. The perceptual brightness is compressed, and the perceptual color scales, i.e., colorfulness and hue are derived from HDR images by employing CIECAM16 color adaptation equations. A psychophysical experiment was conducted to automate the brightness compression parameter. The model employs fully automatic and adaptive approach, obviating the requirement for manual parameter selection. TMOz was evaluated in terms of contrast, colorfulness and overall image quality. The objective and subjective evaluation methods revealed that the proposed model outperformed the state-of-the-art TMOs.