高动态范围视频：从采集到显示与应用详解

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《高动态范围视频：从获取到显示与应用》是一本由弗雷德里克·杜法克斯（Frederic Dufaux）博士，来自法国国家科学研究中心（CNRS）和巴黎综合理工学院（Telecom ParisTech），帕特里克·勒卡莱特（Patrick LeCallet）教授，来自南特大学（University of Nantes），拉法尔·M·马尼亚克（Rafał K. Mantiuk）博士，剑桥大学（University of Cambridge），以及玛尔塔·马雷克（Marta Mrak）女士，英国广播公司（British Broadcasting Corporation）共同编著的书籍。这本书专注于探讨高动态范围（HDR）视频的各个方面，从视频的采集、处理、到其在显示技术和实际应用中的最新进展。 HDR技术是现代影像处理领域的一个重要分支，它旨在捕捉并再现比传统动态范围更大的视觉细节，以更好地模拟人眼对明暗对比的感知。该书详细阐述了HDR视频的理论基础，包括但不限于如何通过新型传感器和算法提高图像的动态范围，以及如何在拍摄、编辑过程中优化高动态场景的表现。书中涵盖了HDR视频的获取技术，例如使用多帧或时间线融合的方法来捕捉更广阔的光照范围，以及如何使用HDR编码标准如HDR10和Dolby Vision来储存和传输高动态范围内容。此外，还深入解析了HDR视频在显示器上的显示技术，包括如何处理HDR信号以适应不同类型的显示器，以及如何通过色彩管理和亮度动态范围调整来提供最佳观看体验。对于实际应用部分，本书讨论了HDR视频在电影、电视、游戏、虚拟现实（VR）和增强现实（AR）等领域的应用案例，以及HDR技术如何提升这些领域的视觉质量和沉浸感。此外，书中可能还会涉及HDR技术面临的挑战，如色彩一致性问题、设备兼容性问题以及如何解决跨平台和跨设备的显示一致性问题。作为学术出版物，这本书不仅提供了深入的技术分析，还可能包含丰富的实验结果和案例研究，以便读者了解HDR视频技术的实际性能和效果。版权方面，所有复制和传播必须得到Elsevier公司的书面许可，有关版权政策和使用权限的信息可以在出版社网站上找到。《高动态范围视频：从获取到显示与应用》是一本全面而权威的指南，对于从事图像处理、视频制作、显示技术或相关领域的专业人士来说，它是理解和掌握HDR技术不可或缺的参考资料。

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4 CHAPTER 1 THE FUNDAMENTAL BASIS OF HDR

FIGURE 1.2

Left: Early video cameras (television cameras), such as this one at the 1936 Summer Olympics, used camera

tubes that could be permanently damaged by overexposure. Image from Wikimedia Commons. Center and

right: Early television camera lenses typically had an iris with a “C” setting, which means that the iris is

completely closed. This was to protect the sensor from being damaged by light. When the camera was not in

use, or was passing into a region of bright light, the lens could be closed to protect the sensor from damage.

Generally, the camera operator had to be very careful to open up the iris only enough for a proper exposure, but

not so far as to permanently damage the camera sensor.

overexposed, thus making it possible to capture images at an extreme range of exposures, and it is

these extreme exposures that allow us to see extreme shadow detail (due to the m assively overexposed

images) and extreme highlight detail (due to having one or more massively underexposed images

as well).

HDR allows us to increase the dynamic range, ideally, all the way up to being equal to the

dynamage range.

1.1.2 THE FUNDAMENTAL PRINCIPLE OF HDR: DYNAMIC RANGE

AND DYNAMAGE RANGE

HDR works whenever the dynamage range of a sensor exceeds its dynamic range.

Dynamic range and dynamage range are deﬁned as follows:

Dynamic range is the ratio between the largest and smallest nonnegative quantity, such as magnitude,

amplitude, energy, or the like, of sound, light, or the like, for which a small incremental difference in

the quantity can still be sensed (ie, the range over which changes in the quantity remain discernible)

(Mann et al., 2011, 2012).

Dynamage range is the ratio between the largest quantity that will not damage a sensor or device or

receiver, and the smallest nonnegative quantity for which changes in the quantity remain discernible

(Mann et al., 2011, 2012).

1.2 HISTORICAL MOTIVATION FOR HDR IMAGING 5

1.1.3 HDR IMAGING TECHNIQUES

HDR imaging is the set of techniques that computationally extend the usual or standard dynamic range

of a signal. HDR imaging has arisen in multiple ﬁelds, such as computational photography, computer

graphics, and animation. HDR signals may be produced in several ways: by the combining of multiple

lower dynamic range signals for HDR reconstruction; synthetically, by simulation or raytracing; or by

use of HDR sensors for data acquisition.

1.1.4 HDR FROM MULTIPLE EXPOSURES

According to Robertson et al. (2003), “the ﬁrst report of digitally combining multiple pictures of the

same scene to improve dynamic range appears to be (Mann, 1993).”

HDR imaging by reconstruction from multiple exposures is deﬁned as follows:

Deﬁnition of HDR reconstruction: The estimation of at least one photoquantity from a plurality of

differently exposed images of the same scene or subject matter (Mann, 1993, 2000, 2001; Mann and

Picard, 1995a; Ali and Mann, 2012; Robertson et al., 2003; Reinhard et al., 2005).

Speciﬁcally, HDR reconstruction returns an estimate of a photoquantity (or sequence of estimates

in the case of video), q(x, y) (any possibly spatially or temporally varying q or q(x), q(x, y, z), q(x, y, t),

or q(x, y, z, t)), on the b asis of a plurality of exposures f

= f (k

q(x, y)), at exposure settings k

,where

there is also noise in each of these exposures f

, through a camera response function, f , which is often

unknown (although it may also be known, o r it may be linear, o r it may be the identity map). The

exposure settings k

may also be unknown.

A separate optional step of tone mapping the photoquantigraph, q, may be taken, if desired — for

example, to produce an output image that can be printed or displayed on low dynamic range (LDR)

output media. In situations where there is no need for a human-viewable HDR image (eg, HDR-

based input to a computer vision system such as the wearable face-recognizer Mann, 1996b), the

photoquantigraph may have direct use without the n eed to convert it to an LDR image.

A typical approach to generate q from f

is to transform each of the input images f

to estimates of

that photoquantity, and then to combine the results with use of a weighted sum (Mann, 1993, 2000,

2001; Mann and Picard, 1995a; Debevec and Malik, 1997; Robertson et al., 2003). Other approaches

are probabilistic in nature, and t ypically use nonlinear optimization (Ali and Mann, 2012; Paletal.,

2004).

1.2 HISTORICAL MOTIVATION FOR HDR IMAGING

HDR reconstruction from m ultiple exposures originated with author S. Mann (described as “the

father of the wearable co mputer,” IEEE Internation a l Solid-State Circuits Conference, February 7,

2000), through a childhood fascination with sensing and metasensing that led to the invention of

the DEG (Fig. 1.3). This includes the use of wearable sensors to process and mediate daily life,

from wearable technologies as a photographic art form (Mann, 1985; Ryals, 1995), to gesture-based

augmented/augmediated reality (AR) (Mann, 1997b) for the capture, enhancement, and rendering of

6 CHAPTER 1 THE FUNDAMENTAL BASIS OF HDR

FIGURE 1.3

Author S. Mann with DEG and a DEG-based HDR welding helmet. Left: Original DEG, in comparison with a

more recent commercial product. Right: Early DEG-based welding helmet prototype. The EyeTap principle was

used to capture eyeward-bound light and process it with HDR video overlays, along with augmediated reality:

augmented in dark areas with 3D computer -generated overlays that adapt to appear where they are least

distracting, while the vision is deliberately diminished in bright areas of the electric welding arc.

everyday experience, a generalized form of self-sensing known as “sousveillance” (“undersight”), in

contrast to the more familiar and commo n practice of “surveillance” (“oversight”). Sou sveillance,

as a ﬁeld of research, has recently expanded greatly, and been given a variety of new names — for

example, “lifelogging,” “quantiﬁed self,” “self-quantifying,’ “self-quantiﬁcation,” “personal imaging,”

“personal informatics,” “personal sensing,” “self-tracking,” “self-analytics,” “autoveillance,” “self-

(sur/sous)veillance,” “body hacking,” “personal media analytics,” and “personal informatics.” Sousveil-

lance also includes metaveillance. Metaveillance is the seeing of sight, visualization of vision, sensing

of sensors, and sensing their capacity to sense (Fig. 1.4). According to Nicholas Negroponte, Founder,

Director,andChairmanoftheMITMediaLab,“SteveMannistheperfectexampleofsomeone...who

persisted in his vision and ended up founding a new discipline.” From Bangor Daily News — Sep. 26,

1997; later appears in Toronto Star — Jul. 8, 2001.

1.3 THEORY OF HDR IMAGING

The theory and practice of quantigraphic image processing, with comparametric equations, arose out

of the ﬁeld of sousveillance (wear able computing, quantimetric self-sensing, etc.) within the context

of mediated reality (Mann, 1997a) and personal imaging (Mann, 1997b). However, it has potentially

much more widespread applications in image processing than just the wearable photographic and

videographic vision systems for which it was developed. Accordingly, a general formulation that does

not necessarily invo lve a wear able camera system will be given. This section follows very closely, if

not identically, that given in Mann (2000) and the textbook Intelligent Image Processing (Mann, 2001).

1.3 THEORY OF HDR IMAGING 7

(A) (B) (C) (D)

FIGURE 1.4

Sousveillance: (A) Sequential Wave Imprinting Machine developed by author S. Mann in the 1970s and early

1980s for making invisible ﬁelds such as sound waves and radio waves, visible, and also to sense sensing

itself (Mann, 1992). In this example, a television receiver (“rabbit ears” antenna) picked up the video signal

from a wireless surveillance camera and ampliﬁed the signal with enough strength to directly drive an array of

35 electric light bulbs. (B) Because of video feedback, the lamps illuminated when they were within the ﬁeld of

view of the surveillance camera. The effect was an AR display rendering the camera’s sightﬁeld (Mann, 2014)

visible to anyone watching the light wand being waved back and forth (no need for special eyewear). A problem

with metasensing is the massive dynamic range present in the sightﬁeld (not just the camera’s ﬁeld/angle of

view, but also the wide variation in visual acuity, so rendered: more light meant more sight, but the light

saturated and made it difﬁcult to see differences in the amount of sight present at each point in space). (C) To

overcome this limitation, pseudocolor metasensing was used. Here multiple-exposure photographs were taken

through various colored ﬁlters while a metasensory device was moved through the space around the sensor

(camera or human eye). Recent work on veillametrics (Janzen and Mann, 2014) also uses pseudocolor to

render HDR metasensory images from a large collection of highly accurate scientiﬁc measurements of camera

or human sight. (D) Author S. Mann, in 1996, with video (electrovisuogram), electrocardiogram,

electroencephalogram, respiration, skin conductivity, and various other sensors that provide a highly complete

capture, processing, recording, and transmission of physiological body function plus surrounding

environmental information.

From Mann, S., 2001. Intelligent Image Processing. John Wiley and Sons, New York, p. 384.

1.3.1 THE WYCKOFF PRINCIPLE AND THE RANGE OF LIGHT

The quantity of light falling on a n image sensor array, or the like, is a real-valued function q(x, y)

of two real variables x and y. An image is typically a degraded measurement of this function, where

degredations may be divided into two categories, those that act on the domain (x, y) and those that act

on the range q. Sampling, aliasing, and blurring act on the domain, while noise (including quantization

noise) and the nonlinear response function of the camera act on the range q.

Registering and combining multiple pictures of the same subject matter will often result in an

improved image of greater deﬁnition. There are four classes of such improvement:

1. increased spatial resolution (domain resolution),

2. increased spatial extent (domain extent),

3. increased tonal ﬁdelity (r ange resolution), and

4. increased dynamic range (range extent).

8 CHAPTER 1 THE FUNDAMENTAL BASIS OF HDR

1.3.2 WHAT’S GOOD FOR THE DOMAIN IS GOOD FOR THE RANGE

The notion of producing a better picture by combining multiple input pictures has been well studied

with regard to the domain (x, y) of these pictures. Horn and Schunk (1981), for example, provided

a means o f determining optical ﬂow, and many researchers have used this result to spatially register

multiple images in order to provide a single image of increased spatial resolution and increased spatial

extent. Subpixel registration methods such as those proposed by Irani and Peleg (1991)andMann

and Picard (1994) attempt to incr ease domain resolution. These methods depend on a slight (subpixel)

shift from one image to the next. Image compositing (mosaicing) methods such as those proposed by

Mann (1993), Mann and Picard (1995c), and Szeliski ( 1996) attempt to increase domain extent.These

methods depend on large shifts from one image to the next.

Methods that are aimed at increasing domain resolution and domain extent tend to also improve

tonal ﬁdelity, to a limited extent, by virtue of a sign al-averaging and noise-reducing effect. However, we

will see in what follows a generalization of the concept of signal averaging called “quantigraphic signal

averaging.” This generalized signal averaging allows images of different exposure to be combined to

further improve on tonal ﬁdelity (range resolution), beyond improvements possible by traditional signal

averaging. Moreover, the proposed method drastically increases dynamic range (range extent). Just as

spatial shifts in the domain (x, y) improve the image, we will also see how exposure shifts (shifts in the

range, q) can, with the proposed method, result in even greater improvements to the image.

1.3.3 EXTENDING DYNAMIC RANGE AND IMPROVEMENT OF RANGE RESOLUTION

BY COMBINING DIFFERENTLY EXPOSED PICTURES OF THE SAME SUBJECT MATTER

The principles of quantigraphic image processing and the notion of the use of differently exposed

pictures of the same subject matter to make a picture composite of extended dynamic range were

inspired by the pio neering work of Wyckoff (1962, 1961), who invented so-called “extended response

ﬁlm.”

Most everyday scenes have a far greater dynamic range than can be recorded on a photographic ﬁlm

or electronic imaging apparatus. However, a set of pictures that are identical except for their exposure

collectively show u s much more dynamic range than any single picture from that set, and also allow the

camera’s response function to be estimated, to within a single constant scalar unknown (Mann, 1993,

1996a; Mann and Picard, 1995b).

A set of functions

(x) = f (k

q(x)), (1.1)

where k

are scalar constants, is known as a Wyckoff set (Mann, 1993, 1996a). A Wyckoff set of

functions f

(x) describes a set o f images differing only in exposure when x = (x, y) is the continuous

spatial coordinate of the focal plane of an electronic imaging array (or piece of ﬁlm), q is the

quantity of light falling on the array (or ﬁlm), and f is the unknown nonlinearity of the camera’s (or

combined ﬁlm’s and scanner’s) response function. Generally, f is assumed to be a pointwise function

(eg, invariant to x).

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高动态范围视频：从采集到显示与应用详解

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