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### 基于亮度驱动感知分组的色调再现：理论与方法 #### 一、引言《基于亮度驱动感知分组的色调再现》一文由Hwann-Tzon Chen、Tyng-Luh Liu和Chiou-Shann Fuh三位学者共同撰写，并发表于2005年的《国际计算机视觉杂志》（International Journal of Computer Vision）上。该论文旨在探讨如何通过整合局部适应效应和保持全局对比度一致性来解决色调再现问题。 #### 二、背景与挑战在计算机图形学和图像处理领域，处理高动态范围(HDR)图像一直是研究的热点之一。HDR图像能够捕捉到更宽广的亮度范围，从而更加真实地模拟人眼所见的真实世界。然而，在将HDR图像转换为适合显示器或打印媒介显示的标准动态范围(SDR)图像时，存在许多技术上的挑战。其中最关键的问题之一是如何在压缩HDR图像的同时，保持图像的自然观感和细节。 #### 三、主要贡献文章提出了一个基于亮度驱动感知分组的方法，该方法通过将HDR图像分解为多个区域，然后对每个区域分别进行色调映射来解决上述问题。这种方法不仅考虑了局部适应性机制，还特别强调了维持全局对比度印象的一致性，从而避免了在压缩HDR图像时出现不自然的视觉效果。 #### 四、关键技术点 1. **亮度驱动感知分组**：该技术的核心在于利用亮度信息将图像分割成若干个具有相似特性的区域。这种分割方式有助于保留图像中的细节，并确保在整个图像中保持一致的视觉印象。 2. **局部适应性机制**：人眼对于不同亮度级别的感知能力是不同的，因此在处理HDR图像时需要考虑到这一点。文中提出的算法通过调整局部区域的亮度来模拟这一特性，从而使得转换后的图像看起来更加自然。 3. **全局对比度一致性**：为了保持图像的整体视觉效果，算法还需要考虑到全局的对比度一致性。这意味着在压缩HDR图像的过程中，不仅要关注局部细节，还要确保整个图像的对比度变化是连贯的。 4. **高效性和参数优化**：文章提到，该方法的另一个优点是计算效率高且不需要复杂的参数微调。这使得该算法非常适合实际应用，尤其是在处理大量HDR图像的情况下。 #### 五、实验结果与比较作者们通过对一系列HDR图像进行实验验证，证明了所提出的方法能够在保持局部细节的同时，较好地维护全局感知印象。与其他方法相比，该框架在平衡局部细节保留和全局感知印象方面表现出了良好的性能。 #### 六、结论《基于亮度驱动感知分组的色调再现》一文提供了一种新颖而有效的解决方案来处理HDR图像的色调再现问题。通过结合亮度驱动的感知分组技术和全局对比度一致性原则，该方法能够在保留图像细节的同时，确保转换后的图像看起来更加自然和谐。这对于计算机图形学、图像处理以及数字摄影等领域都具有重要的意义。

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International Journal of Computer Vision 65(1/2), 73–96, 2005



2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.

DOI: 10.1007/s11263-005-3846-z

Tone Reproduction: A Perspective from Luminance-Driven

Perceptual Grouping

HWANN-TZONG CHEN

Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan;

Department of CSIE, National Taiwan University, Taipei 106, Taiwan

pras@iis.sinica.edu.tw

TYNG-LUH LIU

Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan

liutyng@iis.sinica.edu.tw

CHIOU-SHANN FUH

Department of CSIE, National Taiwan University, Taipei 106, Taiwan

fuh@csie.ntu.edu.tw

Received June 16, 2004; Revised May 24, 2005; Accepted June 29, 2005

First online version published in January, 2006

Abstract. We address the tone reproduction problem by integrating the local adaptation effect with the consistency

in global contrast impression. Many previous works on tone reproduction have focused on investigating the local

adaptation mechanism of human eyes to compress high-dynamic-range (HDR) luminance into a displayable range.

Nevertheless, while the realization of local adaptation properties is not theoretically deﬁned, exaggerating such

effects often leads to unnatural visual impression of global contrast. We propose to perceptually decompose the

luminance into a small number of regions that sequentially encode the overall impression of an HDR image. A

piecewise tone mapping can then be constructed to region-wise perform HDR compressions, using local mappings

constrained by the estimated global perception. Indeed, in our approach, the region information is used not only

to practically approximate the local properties of luminance, but more importantly to retain the global impression.

Besides, it is worth mentioning that the proposed algorithm is efﬁcient, and mostly does not require excessive

parameter ﬁne-tuning. Our experimental results and comparisons indicate that the described framework gives a

good balance in both preserving local details and maintaining global perceptual impression of HDR scenes.

1. Introduction

The attempt to reproduce the visual perception of the

real world is at the heart of painting and photography.

Artists have long been endeavoring to develop skills in

simulating actual reﬂected light within the limitation

of the medium, since our world generally delivers a

much wider range of luminance than pigments can

reﬂect. Apart from artistic concern, recreating real-

scene impressions on limited media is also inevitable

in many vision and graphics applications (see Tumblin

and Rushmeier, 1993 for a more detailed discussion).

Forexample, the contrast of a today’s ﬁne LCD monitor

could be around 1,000 to 1, but we may need to use

it to display an indoor scene comprising very bright

windows that results in a contrast of 250,000 to 1.

By a tone reproduction problem, we are interested in

establishing a method to satisfactorily reconstruct the

74 Chen, Liu and Fuh

high dynamic range (HDR) radiance on a low dynamic

range (LDR) image. The dynamic range of a digital

image typically refers to the contrast ratio between its

brightest and darkest parts, though a more compre-

hensive deﬁnition should include issues such as noise

sensitivity and possible nonlinear distortion in encod-

ing the pixel values. In Ward (1991), Ward proposes a

ﬂoating-point picture format to record HDR radiance

in 32 bits per pixel, and designs a graphics rendering

system to output images in the format. Debevec and

Malik (1997) have shown that HDR radiance of real

scenes may also be captured using regular digital cam-

eras. They describe a technique to combine a series of

pictures taken with different exposure settings into a

single HDR image, called the radiance map.Inthis

context, the focus of our work can be stated as solving

the tone reproduction problem of radiance maps, that

is, generating a displayable standard RGB image that

preserves visually signiﬁcant properties of the original

HDR radiance map.

1.1. Related Work

Several previous works havebeen devoted to producing

HDR images of real scenes. The method by Debevec

and Malik (1997) requires multiple photos of a scene

taken under different exposures. To simultaneously

capture luminance under multiple exposures, Nayar

and Mitsunaga (2000) develop a camera, including a

mask with cells of different optical transparencies. The

grid mask provides different exposures in neighboring

pixels, which are averaged to output an HDR image.

Naturally, panoramic imaging also involves high dy-

namic range. The process of collecting a broader range

of luminance at each location can be coupled with the

process of constructing a wider ﬁeld of view by adding

spatially varying optical ﬁlters to a camera (Aggarwal

and Ahuja, 2001; Schechner and Nayar, 2003). Yet, on

a different focus, Kang et al. (2003) propose to gener-

ate HDR videos by varying the exposure of each frame

and then by stitching consecutive frames.

Concerning displaying HDR images with good per-

ceptual ﬁdelity, tone reproduction addresses visibil-

ity and impression by ﬁnding an appropriate map-

ping to compress the contrast into a displayable range.

For the convenience of our discussion, we categorize

tone-mapping functions of related work into global

mappings and local mappings, according to whether

spatial information is incorporated in their respective

formulations.

1.1.1. Global Tone Mapping. Global tone mappings

are spatially uniform functions. They uniformly com-

press the luminance values of pixels into a displayable

range, regardless of pixels’ spatial or local properties.

The great advantage of using a global mapping is its

efﬁciency Tumblin and Rushmeier, 1993. Since global

mappings are often conveniently designed as mono-

tonically increasing, these functions therefore preserve

the order of pixel luminance values. Such a property

gives another advantage of using global mappings—

for avoiding halo artifacts, which are undesirable in

displaying HDR images. Halos are due to reversed

contrasts in a neighborhood, i.e., an area originally

darker than its neighbors becomes brighter after tone

mapping, or vice versa. However, strictly and glob-

ally preserving the order of luminance is not always

necessary (though sufﬁcient) for avoiding halos, and

may not be preferable in some circumstances. Every

so often we may want to emphasize local contrasts to

display more details, even at the risk of re-arranging the

global order of luminance, as long as no perceivable ar-

tifacts are produced. Ward et al. (1997) describe a more

sophisticated approach to globally adjust contrast with

respect to luminance histograms. Nevertheless, their

method still smooths out image details in areas of ﬂat

histograms.

1.1.2. Local Tone Mapping. To better preserve im-

portant visual features, such as image details, of high

contrast scenes, several tone reproduction methods

have exploited local (or spatially nonuniform) map-

pings, as human visual system operates more likely

this way. Particularly, human visual cells are organized

in a center-surround manner so that we can see a broad

range of luminance values by discriminating locally

(Palmer, 1999). Simulating the local adaptation effect

is a common tactic to keep image details for tone map-

ping. Its basic idea is to attenuate high contrast by

computing locally-averaged luminance values for ef-

fectively adjusting the tone-mapping parameters, e.g.,

the scale factor (Chiu et al., 1993; Tumblin et al., 1999),

or the luminance gain control (Pattanaik et al., 1998).

Since the term adaptation luminance has already been

used in global tone-mapping methods (Ferwerda et al.,

1996; Tumblin and Rushmeier, 1993; Ward, 1994), the

locally-averaged luminance in Ashikhmin (2002) and

Yee and Pattanaik (2003) is referred to as local adap-

tation luminance to emphasize its spatially-varying

property. A related approach to non-uniformly reduc-

ing pixels’ luminance values relies on extracting the

Tone Reproduction: A Perspective from Luminance-Driven Perceptual Grouping 75

illumination from an HDR image (Kimmel et al., 2003;

Tumblin et al., 1999). Since the estimated illumination

usually has a wider dynamic range, a displayable im-

age can be obtained by reducing the dynamic range via

compressing the illumination, and then by recombin-

ing the compressed one with the rest of the original

HDR image.

When displaying synthesized HDR images, the

problem of extracting the layers of illumination and

surface properties is inherently solved because images

in graphics are usually rendered from illumination and

reﬂectance (e.g., Tumblin et al., 1999). In dealing with

images of natural scenes, Land’s Retinex theory (Land

and McCann, 1971) can be used to compute the illu-

mination, and has been implemented with multi-scale

Gaussian ﬁlters (Jobson, 1997). There are other tone-

mapping methods also using multi-scale Gaussian ﬁl-

ters to derive the locally-averaged luminance for sim-

ulating the local adaptation effect (Ashikhmin, 2002;

Pattanaik et al., 1998), or the photographic process

(Reinhard et al., 2002). The concern here is that since

the global order of luminance is no longer guaranteed

by local tone mappings, strategies to avoid halo arti-

facts must be explicitly formulated. To gain insight into

this issue, we look at a scenario of applying a large-

scale Gaussian ﬁlter to smooth two neighboring and

monotone areas that occupy signiﬁcantly different lu-

minance levels. Then, tone mapping with the resulting

over-smoothed adaptation luminance would exagger-

ate the contrast near the boundary of the two areas,

and consequently yield halos. The example suggests

that choosing an appropriate scale of the smoothing

operator is critical for computing the locally-averaged

luminance and avoiding halos. Ashikhmin (2002) and

Reinhard et al. (2002) adopt similar schemes to deter-

mine the scales of Gaussian ﬁlters. At each pixel lo-

cation, they gradually increase the scale of a Gaussian

ﬁlter until that large contrasts are encountered.

Besides multi-scale Gaussians, anisotropic diffu-

sion is another popular choice as the smoothing op-

erator for computing the locally-averaged luminance.

Tumblin and Turk (1999) establish the low curva-

ture image simpliﬁer (LCIS) to blur an HDR image

without smearing the edges. Anisotropic diffusion can

also be implemented in the form of bilateral ﬁltering

(Tomasi and Manduchi, 1998). Speciﬁcally, a bilat-

eral ﬁlter includes two weights (such as Gaussians):

one for the spatial domain, and the other for the range

domain to explain intensity differences between each

pixel and its neighbors. Nearby areas with dissimilar

luminance values thus yield small weights, which pre-

vent from blurring the edges. DiCarlo and Wandell

(2000) have applied this concept to HDR tone map-

ping. More recently, Durand and Dorsey (2002) de-

velop a fast bilateral ﬁltering to efﬁciently compute the

locally-averaged luminance. Though seemingly differ-

ent from the methods mentioned above, the gradient-

domain approach of Fattal et al. (2002) is also a local

tone-mapping. Their formulation emphasizes attenu-

ating large gradients, and recovers the luminance by

solving a Poisson equation.

Since estimating local adaptation luminance relates

to the issues of how to separate dissimilar areas and

how to determine an adequate region for averaging,

it is worthwhile to address these problems from a seg-

mentation viewpoint (Krawczyk et al., 2004; Schlick,

1994; Yee and Pattanaik, 2003). Schlick (1994) has

proposed to divide the picture into zones of similar

intensity values and then compute the average of each

zone. He notes that although image segmentation is

itself a challenging one, the segmentation problem in

tone reproduction is a simpliﬁed case—on a gray-level

image with few regions. Schlick further suggests to

use gradient or histogram thresholding techniques to

partition the image, but concludes without elaborating

further details in Schlick (1994) that the improvement

on anti-aliasing around high contrast is imperceptible.

Yee and Pattanaik (2003) develop a multi-layer parti-

tioning and grouping algorithm to compute the local

adaptation luminance. Their method computes layers

under different intensity resolutions, i.e., to quantize

intensities by different bin widths, and then for each

intensity resolution, to collect pixels of the same bin

into a same group. Each pixel’s value in the local

adaptation luminance can be obtained by averaging the

values of all pixels from the groups in different layers

with which the underlying pixel is associated. Though

not directly related to the problem of HDR tone repro-

duction, the anchoring model proposed by Gilchrist

et al. (1999) also considers the connection between

the decomposition of an image and the perception

of lightness. They propose to decompose a complex

image into multiple local frameworks (in terms of the

Gestalt grouping principles, e.g., see Palmer, 1999,

p. 257). The perceived reﬂectance is estimated by

combining the local and global anchoring properties

according to the decomposed frameworks. For prac-

tically implementing the model of Gilchrist et al., the

main difﬁculties to be overcome include ﬁnding the

right grouping factors to reasonably explain lightness

76 Chen, Liu and Fuh

perception, and establishing a computational approach

to suitably decompose an image for the anchoring.

1.2. Our Approach

We aim to develop an efﬁcient tone-mapping opera-

tor to compress the HDR luminance, accounting for

the revealing of image details and the preserving of

visual impressions. Often these two effects are hard

to be dealt with in a compatible way, but rather as

two competitive factors in formulating a tone-mapping

function. More precisely, since only a limited dynamic

range is allowed to display an image, achieving a

good trade-off between local and global contrast is

not always straightforward. This difﬁculty does reﬂect

in our survey that most recent approaches have fo-

cused mainly on local adaptation properties of tone

reproduction (Ashikhmin, 2002; Durand and Dorsey,

2002; Fattal et al., 2002; Reinhard et al., 2002; Yee and

Pattanaik, 2003), but somewhat overlooked an equally

important issue about maintaining the consistency in

global contrasts. Motivated by this aspect of consid-

eration, we propose to perceptually decompose the

luminance into a small number of regions that en-

code the overall impression based on the sequential

order of luminance levels, and thus term the process

as perceptual grouping for deriving a sparse decom-

position of an HDR image. Accordingly we gain the

advantage of concentrating on designing a local tone

mapping for each region, without the burden of worry-

ing about breaking the overall impressions carried by

the original luminance. Furthermore, it becomes fea-

sible to estimate the local adaptation luminance with

the perceptual-based region information. Having es-

tablished the piecewise tone mapping, we can conve-

niently perform region-wise HDR compressions us-

ing local mappings constrained by the desirable global

property, and produce a displayable LDR image of

good quality.

In the following sections, we shall describe ﬁrst how

we derive the sparse decomposition of an HDR image

based on a luminance-driven grouping process. We

also discuss how the region information is embedded

into the computation of the local adaptation luminance.

We then explain how we region-wise construct a lo-

cal tone-mapping function, and modify the mapping

with the global property encoded in the decomposi-

tion. Finally, we conclude with several experimental

results and comparisons to illustrate the characteristics

of HDR compressions yielded by our method.

2. A Sparse Decomposition for HDR Images

The important link between visual grouping and light-

ness perception in human visual system has been em-

pirically studied in, e.g., Adelson (2000) and Gilchrist

et al. (1999). Still an attempt to devise overall corre-

sponding computational models that explain the theo-

ries of lightness perception and the responses of human

visual system would be extremely challenging, though

worthwhile. For the problem of HDR tone reproduc-

tion, our formulation is not to build a complicated algo-

rithm emulating the machinery/process of human vi-

sual system, but mainly to establish an efﬁcient method

yielding satisfactory results based on useful properties

of the proposed perceptual grouping.

To reduce the dynamic range by tone mapping, we

decompose an HDR image into a small number of re-

gions through a grouping process on luminance, using

a perceptual similarity measure for image retrieval de-

velopedby Rubner and Tomasi (2001) and Rubner et al.

(2000). We regard such a result of perceptual grouping

on luminance as a sparse decomposition for an HDR

image. Besides giving preference to fewer regions, the

eventual decomposition would comprise regions that

are brighter inside and darker near the boundary. As

we will elaborate later, these two properties are essen-

tial to our approach that locally and globally considers

the reduction of dynamic range.

A number of works have explored the idea to com-

pute the local adaptation luminance by referencing im-

age segmentation (e.g., Krawczyk et al., 2004; Schlick,

1994; Yee and Pattanaik, 2003). In particular, while

Schlick (1994) and Krawczyk et al. (2004) take ad-

vantage of existing segmentation techniques, Yee and

Pattanaik (2003) speciﬁcally develop a fast segmen-

tation algorithm for computing local adaptation lumi-

nance. They describe a multi-layer grouping process

(quantized with multiple bin widths in the intensity do-

main) to obtain the locally-averaged luminance value

of each pixel. Nevertheless, all these aforementioned

approaches simply focus on how to estimate local adap-

tation luminance. None of them seeks to use the re-

gion or segmentation information for modulating tone-

mapping functions.

In our formulation the region information is used not

only to compute local adaptation luminance values, but

also to establish more comprehensive tone-mapping

functions for maintaining global contrasts. We shall in-

troduce a new perceptual grouping algorithm that can

fast construct the sparse decomposition for an HDR

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