Image enhancement using a contrast measure in the compressed domain
ABSTRACT An image enhancement algorithm for images compressed using the JPEG standard is presented. The algorithm is based on a contrast measure defined within the discrete cosine transform (DCT) domain. The advantages of the psychophysically motivated algorithm are 1) the algorithm does not affect the compressibility of the original image because it enhances the images in the decompression stage and 2) the approach is characterized by low computational complexity. The proposed algorithm is applicable to any DCT-based image compression standard, such as JPEG, MPEG 2, and H. 261.
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ABSTRACT: Advances in the area of digital chest radiography have resulted in the acquisition of high-quality images of the human chest. With these advances, there arises a genuine need for image processing algorithms specific to the chest, in order to fully exploit this digital technology. We have implemented the well-known technique of histogram equalization, noting the problems encountered when it is adapted to chest images. These problems have been successfully solved with our regionally adaptive histogram equalization method. With this technique histograms are calculated locally and then modified according to both the mean pixel value of that region as well as certain characteristics of the cumulative distribution function. This process, which has allowed certain regions of the chest radiograph to be enhanced differentially, may also have broader implications for other image processing tasks.IEEE Transactions on Medical Imaging 04/1987; · 3.64 Impact Factor
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ABSTRACT: Abstract—This paper presents a new method for unsharp masking for contrast enhancement of images. Our approach employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.
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ABSTRACT: Contrast enhancement is one of the most important issues of image processing, pattern recognition and computer vision. The commonly used techniques for contrast enhancement fall into two categories: (1) indirect methods of contrast enhancement and (2) direct methods of contrast enhancement. Indirect approaches mainly modify histogram by assigning new values to the original intensity levels. Histogram speci"cation and histogram equalization are two popular indirect contrast enhancement methods. However, histogram modi"cation technique only stretches the global distribu-tion of the intensity. The basic idea of direct contrast enhancement methods is to establish a criterion of contrast measurement and to enhance the image by improving the contrast measure. The contrast can be measured globally and locally. It is more reasonable to de"ne a local contrast when an image contains textual information. Fuzzy logic has been found many applications in image processing, pattern recognition, etc. Fuzzy set theory is a useful tool for handling the uncertainty in the images associated with vagueness and/or imprecision. In this paper, we propose a novel adaptive direct fuzzy contrast enhancement method based on the fuzzy entropy principle and fuzzy set theory. We have conducted experiments on many images. The experimental results demonstrate that the proposed algorithm is very e!ective in contrast enhancement as well as in preventing over-enhancement.Pattern Recognition. 01/2000; 33:809-819.
IEEE SIGNAL PROCESSING LETTERS, VOL. 10, NO. 10, OCTOBER 2003 289
Image Enhancement Using a Contrast Measure
in the Compressed Domain
Jinshan Tang, Senior Member, IEEE, Eli Peli, and Scott Acton, Senior Member, IEEE
Abstract—An image enhancement algorithm for images com-
pressed using the JPEG standard is presented. The algorithm is
based on a contrast measure defined within the discrete cosine
transform (DCT) domain. The advantages of the psychophysically
motivated algorithm are 1) the algorithm does not affect the com-
pressibility of the original image because it enhances the images
in the decompression stage and 2) the approach is characterized
by low computational complexity. The proposed algorithm is
applicable to any DCT-based image compression standard, such
as JPEG, MPEG 2, and H. 261.
Index Terms—Compressed domain, contrast measure, discrete
cosine transform (DCT), human vision system, image enhance-
than the original image for a specific application or set of
objectives . Many image enhancement algorithms have
been proposed. One of the most widely used algorithms is
global histogram equalization , which adjusts the intensity
histogram to approximate a uniform distribution. The main
disadvantage of global histogram equalization is that the global
image properties may not be appropriately applied in a local
context . In fact, global histogram modification treats all
regions of the image equally and, thus, often yields poor local
performance in terms of detail preservation. Therefore, several
local image enhancement algorithms have been introduced to
improve enhancement –. Each of these algorithms can
be classified into two types of image enhancement methods
: indirect image enhancement methods and direct image
enhancement methods. The algorithms described in  and
 belong to the class of indirect image contrast enhancement
methods, since they enhance the image without measuring the
contrast. The algorithms described in – are called direct
local contrast enhancement methods because they establish
a criterion of contrast measure and enhance the images by
improving the contrast measurement directly.
HE GOAL of image enhancement is to improve the
image quality so that the processed image is better
S. Acton and J. Tang was supported in part by the National Science Foundation
(NSF) under Grant 0121596, and the work of E. Peli was supported in part by
the National Institutes of Health under Grant EY05957 and Grant EY12890.
The associate editor coordinating the review of this manuscript and approving
it for publication was Dr. Ramanujan Kashi.
J. Tang and S. Acton are with the Department of Electrical and Computer
Engineering, University of Virginia, Charlottesville, VA 22904 USA (e-mail:
E. Peli is with the Schepens Eye Research Institute, Harvard Medical School,
Boston, MA 02114 USA.
Digital Object Identifier 10.1109/LSP.2003.817178
A key step in the direct image enhancement approach is
the establishment of a suitable image contrast measure. For
simple patterns, two definitions of contrast measure have been
frequently used. One is the Michelson contrast measure ; the
other is the Weber contrast measure (see ). The Michelson
contrast measure is used to measure the contrast of a periodic
pattern such as a sinusoidal grating, while the Weber contrast
measure assumes a large uniform luminance background with
a small test target. Both measures are therefore unsuitable
for measuring the contrast in complex images. A number of
contrast measures were proposed for complex images –,
, . A local contrast measure is proposed in , where
the contrast is measured using the mean gray values in two
rectangular windows centered on a given pixel. Another con-
trast measure based on a local analysis of edges is defined in
 and is derived from the definition in .
quency, an image’s spatial frequency content should be consid-
in  explicitly satisfies this requirement. That definition of
band, contrast is defined as the ratio of the bandpass-filtered
image at that frequency to the image lowpass-filtered to an oc-
tave below the same frequency. This multiscale contrast struc-
ture has found wide applications especially in image processing
problems related to the human vision system , .
measure is defined as the ratio of high-frequency content and
low-frequency content in the bands of the DCT matrix. Like the
contrast measure defined in , our contrast measure also has
a multiscale structure that corresponds with the human vision
system. Based on this contrast measure, an image enhancement
veloped. The basic idea of our algorithm is to filter the image
by manipulating the DCT coefficients according to the contrast
measure defined. The proposed algorithm has the following ad-
vantages: 1) the algorithm does not affect the compressibility
of the original image; 2) given a majority of zero-valued DCT
coefficients (after quantization), the algorithm expense is rela-
as JPEG, MPEG 2, and H. 261.
II. IMAGE CONTRAST ENHANCEMENT IN JPEG DOMAIN
A JPEG system is composed of an encoder and a decoder.
In the encoder, the image is first divided into nonoverlapping
1070-9908/03$17.00 © 2003 IEEE
290IEEE SIGNAL PROCESSING LETTERS, VOL. 10, NO. 10, OCTOBER 2003
are quantized using a specified quantization table. Quantiza-
tion of the DCT coefficients is a lossy process, and in this step,
many small coefficients (usually high frequency) are quantized
to zeros. The zig-zag scan of the DCT matrix followed by en-
tropy coding makes use of this property to lower the bit rate re-
tion with the quantization table and inverse-DCT-transformed.
be an 88 block in the original image, and the
DCT transform of it is
. The 2-DCT transformation is ex-
8 blocks. Then, the two-dimensional DCT is computed for
8 block. Once the DCT coefficients are obtained, they
The DCT inverse transformation can be expressed as
From (3), we see that each
corresponding to the
in the output DCT block are arranged left to right, and top
to bottom in order of increasing spatial frequencies in the hori-
zontal and vertical spatial dimensions, respectively.
The spatial frequency properties of the DCT coefficients pro-
vide a natural way to define a contrast measure in the DCT do-
ratio between high-frequency and low-frequency content .
Thus, the contrast measure can be defined as the ratio of high-
and low-frequency content in the bands of the DCT matrix.
We first classify the coefficients into 15 different frequency
bands. The th band is composed of the coefficients with
. A band defined by
approximation to a circle and, thus, selects approximately equal
radial frequencies. Therefore, the image block generated using
(3) by retaining only one band can be thought of as the band-
pass version of the original image block. As the band number
increases, the frequency content of the bandpass image block
corresponds with higher frequencies and, thus, creates a primi-
tive multiscale structure. Our local contrast measure is defined
on each band with band number more than0. The contrast at the
th band () is defined as
represents the contribution
th waveform  and the coefficients
gives a diamond-shaped
Fig. 1.DCT output block.
is the average amplitude over a spectral band. Fig. 1 illustrates
the first and fourth bands and
Here, for the sake of simplicity, we assume that visual acuity is
) in the DCT domain. The contrast measure
band is the ratio of the frequency content of the bandpass image
block obtained by the th band and the frequency content of the
the definition of our contrast measure has a multiscale structure
similar to  and  in a primitive sense.
in the th
B. Image Enhancement in JPEG Domain
There are three ways to enhance the JPEG compressed im-
ages. The first is to enhance the image before compression.
However, there are two disadvantages of this approach. One is
that enhancement will reduce the compressibility of the original
way is to enhance the image after decompression. Because the
the original image, it is often adopted. In this letter, we consider
direct enhancement in the compressed domain. The basic idea
coefficients. Compared with the image enhancement in the spa-
tial domain, this method can reduce storage requirements and
computational expense as the majority of the coefficients in the
DCT domain are zeros after quantization.
The proposed image enhancement algorithm is based on the
contrast measure proposed in Section II-A. Let the contrast
of the original block be
the contrast at a specific frequency band corresponding to
and let the contrast of the enhanced block be denoted by
. If, for example, one wishes to enhance
the contrast uniformly for all frequencies, then
TANG et al.: IMAGE ENHANCEMENT USING A CONTRAST MEASURE IN THE COMPRESSED DOMAIN291
(d) The proposed contrast-measure-based method with ? ? ????.
Enhanced images using different algorithms. (a) Decompressed JPEG image. (b) Global histogram equalization. (c) Local histogram equalization .
Equation (8) can be stated as
From (9), we can obtain the enhanced DCT coefficients
can be obtained by recursion. The pro-
posed algorithm can be summarized as follows.
292IEEE SIGNAL PROCESSING LETTERS, VOL. 10, NO. 10, OCTOBER 2003
image using alpha-rooting algorithm with ? ? ????.
Step 1. Let ? ? ?,???? ? ??? and
??? ? ?? ? ??????
Step 2. Let ?
Step 3. Use (11) to obtain????? (? ? ? ? ?).
Step 4. If ??
??, use (5) and (9) to compute
?? and ???, respectively. Else, end.
Step 5. Return to Step 2.
? ? ? and use (10) to compute
Hereis an image enhancement control factor that is chosen
by the user. When
, the image will be enhanced. When
, the image will be softened.
III. EXPERIMENTAL RESULTS AND DISCUSSION
In the experiments provided here, a JPEG compressed image
was used to evaluate the performance of the proposed algo-
rithm. The decompressed image without enhancement is shown
in Fig. 2(a). The input image has a gray resolution of eight bits.
The size of the image is 256
The enhanced images obtained by global histogram equal-
ization, local histogram equalization  (with a window size of
30), and the proposed method are shown in Fig. 2(b)–(d),
respectively. In Fig. 2(d), the value of enhancement factor
was decided by a subjective test. When compared with
the original image, the histogram equalization methods and the
proposed method produced moderatelyenhancedimages. How-
ever, in our judgment, the proposed method obtained an en-
hanced image with improved visual quality compared to both
of the histogram equalization methods. With histogram equal-
lightened [see Fig. 2(b) and (c)]. The visual quality of the en-
hanced image obtained by global histogram equalization is su-
perior to that obtained by local histogram equalization as the
local enhancement artificially overemphasizes local details.
the alpha-rooting algorithm , . In this algorithm, the
magnitude of each DCT coefficient is raised to a power
is a positive real number). Let
modified DCT coefficient
is expressed as 
be the DCT coefficients; the
gorithm. The value of
used in our experiments is 0.98, which
from the proposed method with the alpha-rooting algorithm,
one can see that the image obtained with the contrast-measure-
based method retains more detail than the image obtained with
the alpha-rooting algorithm. The image obtained by the alpha-
rooting algorithm is darker than the original image when ob-
served on the screen; however, the printed version differs (the
difference between the screen view and the printed version is
due to the nonlinear gamma response of the monitor).
In this letter, we have described an image contrast enhance-
DCT domain. The comparative analysis between the proposed
algorithm and two existing algorithms has shown the merit of
the contrast measure-based approach.
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Chicago, IL: Univ. Chicago Press,
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