  High Dynamic Range (HDR) images are the ones that contain a greater range of
luminosity as compared to the standard images. HDR images have a higher detail
and clarity of structure, objects, and color, which the standard images lack.
HDR images are useful in capturing scenes that pose high brightness, darker
areas, and shadows, etc. An HDR image comprises multiple narrow-range-exposure
images combined into one high-quality image. As these HDR images cannot be
displayed on standard display devices, the real challenge comes while
converting these HDR images to Low dynamic range (LDR) images. The conversion
of HDR image to LDR image is performed using Tone-mapped operators (TMOs). This
conversion results in the loss of much valuable information in structure,
color, naturalness, and exposures. The loss of information in the LDR image may
not directly be visible to the human eye. To calculate how good an LDR image is
after conversion, various metrics have been proposed previously. Some are not
noise resilient, some work on separate color channels (Red, Green, and Blue one
by one), and some lack capacity to identify the structure. To deal with this
problem, we propose a metric in this paper called the Tone Mapping Quality
Index (TMQI-3), which evaluates the quality of the LDR image based on its
objective score. TMQI-3 is noise resilient, takes account of structure and
naturalness, and works on all three color channels combined into one luminosity
component. This eliminates the need to use multiple metrics at the same time.
We compute results for several HDR and LDR images from the literature and show
that our quality index metric performs better than the baseline models.
