Blind Quality Assessment of Tone-Mapped Images Via Analysis of Information, Naturalness, and Structure

Abstract

High dynamic range (HDR) imaging techniques have been working constantly, actively, and validly in the fault detection and disease diagnosis in the astronomical and medical fields, and currently they have also gained much more attention from digital image processing and computer vision communities. While HDR imaging devices are starting to have friendly prices, HDR display devices are still out of reach of typical consumers. Due to the limited availability of HDR display devices, in most cases tone mapping operators (TMOs) are used to convert HDR images to standard low dynamic range (LDR) images for visualization. But existing TMOs cannot work effectively for all kinds of HDR images, with their performance largely depending on brightness, contrast, and structure properties of a scene. To accurately measure and compare the performance of distinct TMOs, in this paper develop an effective and efficient no-reference objective quality metric which can automatically assess LDR images created by different TMOs without access to the original HDR images. Our model is shown to be statistically superior to recent full- and no-reference quality measures on the existing tone-mapped image database and a new relevant database built in this work.