This paper proposes a nonparametric steganalysis method for quantization index modulation (QIM) based steganography. The proposed steganalysis method uses irregularity (or randomness) in the test-image to distinguish between the cover- and the stego-image. We have shown that plain-quantization (quantization without message embedding) induces regularity in the resulting quantized-image; whereas message embedding using QIM increases irregularity in the resulting QIM-stego image. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test-image. Simulation results presented in this paper show that the proposed
steganalysis technique can distinguish between the cover- and the stego-image with low false rates (i.e. Pfp < 0.1
& Pfn < 0.07 for dither modulation stego and Pfp < 0.12 & Pfn < 0.002 for QIM-stego).
KEYWORDS: Data hiding, Distortion, Quantization, Digital watermarking, Computer programming, Binary data, Signal to noise ratio, Multimedia, Telecommunications, Interference (communication)
Most of quantization based data hiding schemes are based on uniform scalar quantizer, which is optimal only if the host signal is uniformly distributed. In our recent work, we proposed two pdf-matched quantizer based data hiding schemes, both of which have better embedding efficiency than conventional uniform quantizer based schemes. But these schemes require a lot of location information for qualified pixels, which increases the side information needed by the decoder. Moreover, these schemes are inconvenient when high embedding rates are desired. In this paper, we propose a trellis coded quantizer (TCQ) based data hiding scheme, which is proved to be more embedding efficient than traditional quantizer based schemes. Experimental results show that the
proposed scheme can be up to 1.2 dB closer to the capacity upper bound than the QIM scheme over the whole watermark noise rate (WNR) range. Comparing with our previous work, the proposed TCQ based scheme has less side information and can easily realize a high embedding rate.
KEYWORDS: Data hiding, Distortion, Digital watermarking, Binary data, Quantization, Signal to noise ratio, Computer programming, Interference (communication), Multimedia, Telecommunications
Today, data hiding has become more and more important in a variety of applications including security. Since
Costa's work in the context of communication, the set of quantization based schemes have been proposed as one
class of data hiding schemes. Most of these schemes are based on uniform scalar quantizer, which is optimal
only if the host signal is uniformly distributed. In this paper, we propose pdf -matched embedding schemes,
which not only consider pdf -matched quantizers, but also extend them to multiple dimensions. Specifically,
our contributions to this paper are: We propose a pdf-matched embedding (PME) scheme by generalizing the
probability distribution of host image and then constructing a pdf-matched quantizer as the starting point.
We show experimentally that the proposed pdf-matched quantizer provides better trade-offs between distortion
caused by embedding, the robustness to attacks and the embedding capacity. We extend our algorithm to embed
a vector of bits in a host signal vector. We show by experiments that our scheme can be closer to the data
hiding capacity by embedding larger dimension bit vectors in larger dimension VQs. Two enhancements have
been proposed to our method: by vector flipping and by using distortion compensation (DC-PME), that serve
to further decrease the embedding distortion. For the 1-D case, the PME scheme shows a 1 dB improvement
over the QIM method in a robustness-distortion sense, while DC-PME is 1 dB better than DC-QIM and the 4-D
vector quantizer based PME scheme performs about 3 dB better than the 1-D PME.
Most video coding standards, including MPEG-4, use variable length
codes to encode the discrete cosine transform (DCT) coefficients
of intra frames and inter frames, and the motion vector
information. Although variable length codes can achieve good
compression, they are very sensitive to channel errors, especially
in wireless channels, which are characterized by bursty errors.
Joint source-channel decoding (JSCD) is emerging as a potential
alternative to traditional error resilience methods for dealing
with this sensitivity to channel errors. This paper describes one
such JSCD for the reliable transmission of MPEG-4 video over
wireless channels. We apply the maximum a posteriori probability based JSCD developed for first order Markov sources to both inter and intra coded macroblocks. Experiments indicate that the proposed decoder gives significant improvements (9 dB in some cases) for MPEG-4 video with error resilience at various channel error rates.
KEYWORDS: Wavelets, Video, Video compression, 3D video compression, Video coding, Scalable video coding, 3D video streaming, Image compression, Signal to noise ratio, Wavelet transforms
In this paper we propose a scalable video coding scheme that
utilizes the embedded block coding with optimal truncation
(EBCOT) compression algorithm. Three dimensional spatio-temporal
decomposition of the video sequence succeeded by compression
using the EBCOT generates a SNR and resolution scalable bit
stream. The proposed video coding algorithm not only performs closer to the MPEG-4 video coding standard in compression efficiency but also provides better SNR and resolution scalability. Experimental results show that the performance of the proposed algorithm does better than the 3-D SPIHT (Set Partitioning in Hierarchial Trees)
algorithm by 1.5dB.
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