Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT9.7
Image Retargeting in Compressed Domain
Murthy, O.v. Ramana, Nanyang Tech. Univ.
Muthuswamy, Karthik, Nanyang Tech. Univ.
Rajan, Deepu, Nanyang Tech. Univ.
Chia, Liang-Tien, Nanyang Tech. Univ.
A simple algorithm for image retargeting in the compressed domain is proposed. Most existing retargeting algorithms
work directly in the spatial domain of the raw image. Here, we work on the DCT coefficients of a JPEG-compressed image
to generate a gradient map that serves as an importance map to help identify those parts in the image that need to be
retained during the retargeting process. Each 8x8 block of DCT coefficients is scaled based on the least importance value.
Retargeting can be done both in the horizontal and vertical directions with the same framework. We also illustrate image
enlargement using the same method. Experimental results show that the proposed algorithm produces less distortion in
the retargeted image compared to some other algorithms reported recently.
13:30-16:30, Paper ThBCT9.8
Progressive MAP-Based Deconvolution with Pixel-Dependent Gaussian Prior
Tanaka, Masayuki, Tokyo Inst. of Tech.
Kanda, Takafumi, Tokyo Inst. of Tech.
Okutomi, Masatoshi,
A deconvolution is a fundamental technique and used in various vision applications. A maximum a posteriori estimation
is known as a powerful tool. In this paper, we propose a progressive MAP-based deconvolution algorithm with a pixel dependent
Gaussian image prior. In the proposed algorithm, a mean and a variance for each pixel are adaptively estimated.
Then, the mean and the variance are progressively updated. We experimentally show that the proposed algorithm is comparable
to the state-of-the-art algorithms in the case that the true point spread function (PSF) is used for the deconvolution,
and that the proposed algorithm outperforms in the non-true PSF case.
13:30-16:30, Paper ThBCT9.9
A Fast Image Inpainting Method based on Hybrid Similarity-Distance
Liu, Jie, Chinese Acad. of Sciences
Zhang, Shuwu, Chinese Acad. of Sciences
Yang, Wuyi, Chinese Acad. of Sciences
Li, Heping, Chinese Acad. of Sciences
A fast image in painting method based on hybrid similarity-distance is proposed in this paper. In Criminisi et al.’s work
[1], similarity distance are not reliable enough in many cases and the algorithm performs inefficiently. To solve these problems,
we propose a new searching strategy to accelerate the algorithm. In addition, we modify the confidence-updating
rule to make more reasonable the distributions of the confidences in source region. Besides, taking account of the stationarity
of texture and the reliability of the source regions, we present a hybrid similarity-distance, which combines the
distance in color space with the distance in spatial space by weight coefficients related to the confidence value. A more
reasonable patch will be found out by this hybrid similarity-distance. The experiments verify that the proposed method
yields qualitative improvements compared to Criminisi et al.’s work [1].
13:30-16:30, Paper ThBCT9.10
Reversible Integer 2-D Discrete Fourier Transform by Control Bits
Dursun, Serkan, Univ. of Texas at San Antonio
Grigoryan, Artyom M., Univ. of Texas at San Antonio
This paper describes the 2-D reversible integer discrete Fourier transform (RiDFT), which is based on the concept of the
paired representation of the 2-D image, which is referred to as the unique 2-D frequency and 1-D time representation. The
2-D DFT of the image is split into a minimum set of short transforms, and the image is represented as a set of 1-D signals.
The paired 2-DDFT involves a few operations of multiplication that can be approximated by integer transforms, such as
one-point transforms with one control bit. 24 control bits are required to perform the 8x8-point RiDFT, and 264 control
bits for the 16x16-point 2-D RiDFT of real inputs. The fast paired method of calculating the 1-D DFT is used. The computational
complexity of the proposed 2-D RiDFTs is comparative with the complexity of the fast 2-D DFT.
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