2842 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 7, JULY 2014
QR Images: Optimized Image
Embedding in QR Codes
Gonzalo J. Garateguy, Student Member, IEEE, Gonzalo R. Arce, Fellow, IEEE ,
Daniel L. Lau, Senior Member, IEEE, and Ofelia P. Villarreal, Member, IEEE
Abstract—This paper introduces the concept of QR images,
an automatic method to embed QR codes into color images
with bounded probability of detection error. These embeddings
are compatible with standard decoding applications and can
be applied to any color image with full area coverage. The
QR information bits are encoded into the luminance values of the
image, taking advantage of the immunity of QR readers against
local luminance disturbances. To mitigate the visual distortion
of the QR image, the algorithm utilizes halftoning masks for
the selection of modified pixels and nonlinear programming
techniques to locally optimize luminance levels. A tractable model
for the probability of error is developed and models of the
human visual system are considered in the quality metric used to
optimize the luminance levels of the QR image. To minimize the
processing time, the optimization techniques proposed to consider
the mechanics of a common binarization method and are designed
to be amenable for parallel implementations. Experimental
results show the graceful degradation of the decoding rate and
the perceptual quality as a function the embedding parameters.
A visual comparison between the proposed and existing methods
is presented.
Index Terms—QR codes, image embedding, halftoning.
I. I NTRODUCTION
Q
UICK response (QR) codes [1], [2] have rapidly
emerged as a widely used inventory tracking and identi-
fication method in transport, manufacturing, and retail indus-
tries [3]. Their popularity is due to the proliferation of smart
phones, capable of decoding and accessing on line resources
as well as its high storage capacity and speed of decoding.
QR codes are used in a variety of applications, such as
accessing websites, download personal card information, post
information to social networks, initiate phone calls, reproduce
videos or open text documents. This versatility makes them a
valuable tool in any industry that seeks to engage mobile users
from printed materials. Not surprisingly QR codes have been
widely adopted in the marketing and publicity industry thanks
Manuscript received September 6, 2013; revised December 31, 2013 and
April 14, 2014; accepted April 16, 2014. Date of publication May 2, 2014; date
of current version May 20, 2014. The associate editor coordinating the review
of this manuscript and approving it for publication was Dr. Karsten Mueller.
G. J. Garateguy is with Mathworks, Natick, MA 01760 USA (e-mail:
ggarateg@udel.edu).
G. R. Arce and O. P. Villarreal are with the Department of Electrical and
Computer Engineering, University of Delaware, Newark, DE 19711 USA
(e-mail: ggarateg@udel.edu; ofeliap.villarreal@gmail.com).
D. L. Lau is with the Department of Electrical and Computer Engi-
neering, University of Kentucky, Lexington, KY 40506 USA (e-mail:
dllau@engr.uky.edu).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIP.2014.2321501
to the advantage they provide in tracking the performance of
publicity campaigns.
An important problem of QR codes is its impact on the
aesthetics of publicity designs. The square shapes and limited
color tolerance, severely impairs their integration into billboard
designs or printed materials. This challenge has generated
great interest for algorithms capable of embedding QR codes
into images without loosing decoding robustness. There have
been several efforts to improve the appearance of such embed-
dings [4]–[15] which can be classified in two categories,
methods that modify the luminance or color of image pixels
and methods that replace QR modules.
The methods presented in [6] and [7] base the strategy on
finding the best group of QR modules to substitute by the
image or logo in the QR code. Other methods take advantage
of unused modules in the padding regions [8]–[10] to introduce
the image without affecting the decoding robustness. In [7]
the impact of replacing code modules with image pixels was
studied. The authors concluded that to retain high rate of
decodability, the ratio between image and code area should
be approximately proportional to the correction capacity of
the code. It was also found that superimposing images over
finder or alignment patterns severely decrease the probability
of correct decoding. As a consequence it is common for logos
or images to be located at the center of the code for these
methods as depicted in Fig. 1(a). In general these approaches
do not take advantage of the codeword generation process and
this imposes restrictions in the location of modified modules.
This problem was addressed by recently developed techniques
[4], [11], [12] which manipulate the Reed Solomon encoding
procedure to maximize the area coverage without reducing the
correction capacity.
The second category of embedding algorithms is based on
the modification of the pixel’s luminance. The approach in [5]
chooses central pixels of each module to modify its luminance
since this is the area usually sampled by the decoder. This
approach provides an adequate trade off between robustness
and visual distortion but the number of center pixels modified
is in general a large proportion of the module area and creates
undesirable low pass artifacts [see Fig. 1(c)]. A method which
implements a similar idea is LogoQ [13]. In [16] a method
to introduce colors into QR codes was proposed with the
goal of increasing its data capacity and a detailed study of
the interference between different color layers was presented.
Two embedding methods which are related to the method
presented here are [14] and [15]. In [14] the author chooses the
modified pixels in the code based on the average luminance of
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