Tone Mapping: Tone Mapping: Illuminating Perspectives in Computer Vision

By Fouad Sabry

What is Tone Mapping

Tone mapping is a technique used in image processing and computer graphics to map one set of colors to another to approximate the appearance of high-dynamic-range (HDR) images in a medium that has a more limited dynamic range. Print-outs, CRT or LCD monitors, and projectors all have a limited dynamic range that is inadequate to reproduce the full range of light intensities present in natural scenes. Tone mapping addresses the problem of strong contrast reduction from the scene radiance to the displayable range while preserving the image details and color appearance important to appreciate the original scene content.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Tone_mapping

Chapter 2: Gamma_correction

Chapter 3: Multi-exposure_HDR_capture

Chapter 4: High-dynamic-range_rendering

Chapter 5: Shadow_and_highlight_enhancement

Chapter 6: High_dynamic_range

Chapter 7: Tone_reproduction

Chapter 8: Luminance_HDR

Chapter 9: Aurora_HDR

Chapter 10: EasyHDR

(II) Answering the public top questions about tone mapping.

(III) Real world examples for the usage of tone mapping in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Tone Mapping.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Apr 28, 2024

Book preview

Chapter 1: Tone mapping

To achieve the impression of high dynamic range images in a medium with a lower dynamic range, tone mapping is employed in image processing and computer graphics. Neither printouts nor CRT or LCD monitors nor projectors are capable of accurately reproducing the complete range of light intensities found in real-world settings due to their restricted dynamic range. Tone mapping is a technique that preserves the image details and color look crucial to appreciating the original scene content while reducing the dramatic contrast between the scene brightness and the displayable range.

Inverse tone mapping is a method of increasing an image's dynamic range by shifting its brightness values in the opposite direction.

Since it was challenging to capture the vast range of lighting present in the real world on a chemically constrained negative, the advent of film-based photography caused problems.

Early film developers attempted to remedy this issue by designing the film stocks and the print development systems that gave a desired S-shaped tone curve with slightly enhanced contrast (about 15%) in the middle range and gradually compressed highlights and shadows [1].

The establishment of Air Defense Zones, which adjusts exposure and develop time according on the amount of shadow detail needed (thus controlling highlight tones) increased the grayscale's tonal range (and later, Increasing the dynamic range of color negative film from its normal seven stops to 10.

Photographers have also used dodging and burning to overcome the limitations of the print process [2].

The development of digital photography offered the possibility of more effective responses to this issue.

In 1971, Land and McCann used an early version of an algorithm called Retinex, inspired by theories of lightness perception [3].This method is inspired by the eye’s biological mechanisms of adaptation when lighting conditions are an issue.

There was also a lot of research done on gamut mapping algorithms for color printing.

To foretell how colors might appear, researchers turned to computational models like CIECAM02 and iCAM.

Despite this, If color and tone mapping algorithms are inadequate,, There was still a demand for a talented artist, just like in the editing room of a movie theater.

With the advent of high-contrast computer graphics technology, the primary limitation of displays switched from color to brightness. To adapt HDR (high dynamic range) photos for regular monitors, many tone mapping operators were created. Recent developments in this area have expanded beyond the use of brightness to boost contrast in favor of techniques like user-assisted image replication. Display-driven solutions are currently the norm for picture reproduction since modern displays incorporate sophisticated image processing algorithms that enhance power efficiency, expand color gamut and dynamic range, and adjust image rendering based on environmental factors.

Tone mapping's declared purpose can vary greatly between implementations. There are applications where the primary focus is on creating images that are merely visually pleasant, while others place a greater premium on accurately replicating as many image elements as possible or increasing contrast. Even if the display device cannot recreate the complete range of brightness values, the goal in realistic rendering applications may be to obtain a perceived match between the real scene and the presented image.

Various tone mapping operators have been developed in the recent years.[4] They all can be divided in two main types:

non-linear functions that are based on the image's global variables such as brightness and other global factors make up global (or spatially uniform) operators. After an image's optimum function has been computed, all of its pixels are mapped in the same way regardless of their neighbors' values. These methods are quick and easy to apply (they can be done with look-up tables), but they may reduce contrast. Reduced contrast and inverted colors are two examples of popular global tone mapping techniques.

local (or spatially variable) operators: the non-linear function's parameters vary from pixel to pixel based on retrieved features from neighboring parameters. In other words, the algorithm's impact varies from pixel to pixel based on the characteristics of the surrounding area. Since human vision is primarily sensitive to local contrast, these algorithms are more complex than global ones; they can display artifacts (such as halo effect and ringing); and the output can look unnatural; nonetheless, they can (if applied correctly) deliver the best performance.

A simple example of global tone mapping filter is {\displaystyle V_{\text{out}}={\frac {V_{\text{in}}}{V_{\text{in}}+1}}} (Reinhard), where Vin is the luminance of the original pixel and Vout is the luminance of the filtered pixel.

This function will