Quantization Step Size Research Articles

An efficient and secure robust watermarking scheme for document images using Integer wavelets and block coding of binary watermarks

A novel, efficient and robust watermarking scheme for protection of document image contents is proposed in this work. An integer wavelet-based watermarking scheme for embedding the compressed version of the binary watermark logo has been developed for robust watermarking. At the sender side, the source document image is divided into empty and non-empty segments depending on the absence or presence of the information. Watermarking is applied for non-empty segments and thus the amount of embedding capacity is reduced. A binary watermark logo is compressed using binary block coding technique of appropriate block-size. A level-2 integer wavelet transformation is applied on the non-empty segment of the source document image. LL-sub-band of level-2 of the transformed image is subdivided into blocks of uniform size and compressed watermark bitstream is embedded into it. The compressed watermark is redundantly embedded into blocks using quantization technique. Thus, multiple copies of compressed watermark are available and each block of the source document image segment need not include the entire compressed watermark stream. At the receiver side, the extracted segments from each set of blocks are merged to obtain a single extracted bitstream. The bitstream is further decoded to get the binary watermark. The extracted and embedded watermarks are compared and authentication decision is taken based on majority voting technique. Based on the quantization step size, size of the logo and the level of wavelet transform, the watermarks are extracted without accessing the original image. The experimental results show that the proposed technique is highly robust. The performance of the proposed approach is measured in parameters Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). Results show that the proposed approach is better than the existing methods. In the proposed scheme for decompression of watermark, the level of block coding technique is the key, which provides an additional layer of security.

Improved logarithmic spread transform dither modulation using a robust perceptual model

In the quantization-based watermarking framework, the perceptual just noticeable distortion (JND) model has been widely used to determine the quantization step size, as it can be used for the bette...

Effectively lossless subspace appearance model compression using prior information

Subspace appearance models are widely used in computer vision and image processing tasks to compactly represent the appearance variations of target objects. In order to ensure algorithm performance, they are typically stored in high-precision formats; this results in a large storage footprint, rendering redistribution costly and difficult. Since for most image and vision applications, pixel values are quantized to 8 bits by the acquisition apparatuses, we show that it is possible to construct a fixed-width, effectively lossless representation of the bases vectors, in the sense that reconstructions from the original bases and from the quantized bases never deviate by more than half of the quantization step-size. In addition to directly applying this result to losslessly compress individual models, we also propose an algorithm to compress appearance models by utilizing prior information on the modeled objects in the form of prior appearance subspaces. Experiments conducted on the compression of person-specific face appearance models demonstrate the effectiveness of the proposed algorithms.

Stability and Performance Analysis of Bit-Stream-Based Feedback Control Systems

Bit stream (BS)-based feedback control systems often use a delta–sigma modulator ( $\Delta\Sigma$ -M) as a two-level dynamic quantizer to encode and decode feedback signals. The stability and performance of such systems are critically dependent on the selection of the quantizer step size. This paper derives the stability condition of a BS-based control system in terms of the quantizer step size using sliding mode analysis. It is proved that the quantized system is equivalent to the original system on the sliding manifold under ideal sliding. However, the presence of the quantizer in a $\Delta\Sigma$ -M introduces noise into the system that often degrades the performance of the overall system. This paper therefore determines the optimum quantizer gain, i.e., the upper and lower boundaries of the quantizer, which maintains the stability and reduces the quantization noise. BS-based proportional–integral–differential (BS-PID) controllers are designed using the proposed optimal dynamic quantizer and implemented using three different realizations. Simulation results show that the optimal quantizer significantly reduces the noise within the system bandwidth. The effectiveness of the BS-PID controller with the optimal quantizer is further demonstrated using the experimental prototype of a dc servomechanism by designing a BS-PID controller. The experimental results from a laboratory prototype illustrate that the BS-PID controller gives identical performance to a traditional PID controller and effectively controls the system while consuming less information rate (channel interfaces) and hardware resources.

Improved spread transform dither modulation based on robust perceptual just noticeable distortion model

In the quantization-based watermarking framework, the perceptual just noticeable distortion (JND) model has been widely used to determine the quantization step size, as it can provide a better tradeoff between fidelity and robustness. However, the calculated JND values can vary due to changes introduced by watermark embedding. As a result, the mismatch problem will lead to watermark extraction errors in the absence of attacks. We present an improved spread transform dither modulation (STDM) watermarking scheme. Performance improvement with respect to the existing algorithm is obtained by a discrete cosine transform (DCT)-based perceptual JND model that is highly compatible with the STDM watermarking algorithm. The proposed scheme not only incorporates various masking effects of human visual perception, but also avoids the mismatch problem by utilizing a new measurement of the pixel intensity and edge strength. In contrast to conventional JND models, the proposed model can be theoretically invariant to the changes in the watermark-embedding processing, therefore, more fit for quantization-based watermarking. Experimental results confirm the improved robustness performance of the JND model in the STDM watermarking framework. Simulation results show that the proposed scheme is more robust than the existing JND model-based watermarking algorithms with uniform fidelity. Furthermore, our proposed scheme has a superior performance compared with previously proposed perceptual STDM schemes.

Q-STAR: a perceptual video quality model considering impact of spatial, temporal, and amplitude resolutions.

In this paper, we investigate the impact of spatial, temporal, and amplitude resolution on the perceptual quality of a compressed video. Subjective quality tests were carried out on a mobile device and a total of 189 processed video sequences with 10 source sequences included in the test. Subjective data reveal that the impact of spatial resolution (SR), temporal resolution (TR), and quantization stepsize (QS) can each be captured by a function with a single content-dependent parameter, which indicates the decay rate of the quality with each resolution factor. The joint impact of SR, TR, and QS can be accurately modeled by the product of these three functions with only three parameters. The impact of SR and QS on the quality are independent of that of TR, but there are significant interactions between SR and QS. Furthermore, the model parameters can be predicted accurately from a few content features derived from the original video. The proposed model correlates well with the subjective ratings with a Pearson correlation coefficient of 0.985 when the model parameters are predicted from content features. The quality model is further validated on six other subjective rating data sets with very high accuracy and outperforms several well-known quality models.

Distributed dynamic consensus under quantized communication data

Distributed dynamic average consensus is investigated under quantized communication data. We use a uniform quantizer with constant quantization step-size to deal with the saturation caused by the dynamic consensus error and propose a communication feedback-based distributed consensus protocol suitable for directed time-varying topologies to make the internal state of each agent's encoder consistent with the output of its neighbors' decoder. For the case where the communication topology is directed, balanced and periodically connected, it is shown that if the difference of the reference inputs satisfies some boundedness condition, then the designed quantized dynamic consensus protocol can ensure the states of all the agents achieve dynamic average consensus with arbitrarily small steady state error by properly choosing system parameters. The lower bound of the required quantization levels and the method to choose the system parameters are also presented. Copyright © 2014 John Wiley & Sons, Ltd.

Robust Adaptive Video Watermarking Algorithm based on Dual Transform Domains

In this paper, we propose a robust video watermarking algorithm based on dual transform domain using the adaptive quantization method. In order to increase the robustness of watermark, Particle Swarm Optimization algorithm is used for embedding watermarks. Firstly, one-level wavelet decomposition is employed for the two-dimensional carrier signal by using the characteristics of the wavelet multi-resolution analysis, and then the lowfrequency and middle-frequency coefficients are extracted. After dividing the coefficients into small blocks, discrete cosine transform is applied to each block coefficient. Then, the adaptive quantization algorithm is designed. The optimal embedding positions in the two dimensional space of the carrier signal are determined dynamically by using Particle Swarm Optimization algorithm, and the coefficients in the optimal embedding positions are also extracted. According to the characteristics of these coefficients, the optimal quantization step sizes are determined dynamically in three dimensional space. Finally, the watermark is embedded into watermarking vector adaptively by using the optimal embedding positions and optimal quantization step sizes, then the watermarked vector will be received. The experimental results show that compared with traditional watermark algorithms, the presented robust watermark algorithm not only eliminate the block effects that might arise in the transform process, but also make full use of Particle Swarm Optimization algorithm to determine the optimal embedding positions and optimal quantization step sizes of the watermark embedding algorithm. It has implemented the adaptive embedding and increased the non-sentience and robustness of the watermark. A series of attacks and comparative experiments show that the performance of the proposed algorithm is better than traditional algorithms.

Distributed near lossless compression algorithm for hyperspectral images

This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian–Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian–Wolf decoder. Slepian–Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression.

Finite resolution and serial correlations in mass metrology

In this study the influence of the digital resolution on the properties of a data sample is experimentally determined in mass measurements. A mass comparator with adjustable digital resolution interval, also known as the quantization step size, was used in well controlled repeatable conditions. The same measurement procedure with the resolution differing by up to a factor of 200 was repeatedly carried out, and at least 150 mass differences were recorded with every resolution setting. A clear relationship was observed between the digital resolution and the type of random process characteristics for the data sample. Analysis shows that a white noise process dominates for data sets measured with the smallest digital resolution step from 0.001 mg and up to 0.005 mg. For resolutions from 0.01 mg to 0.2 mg random walk noise is observed for which variance of the sample mean can increase proportionally with sample size. We demonstrate that instrumental resolution is a strictly limiting factor in mass measurements only for the data sets with significant positive correlations, such as those having random walk noise. Otherwise for the white noise process, the smallest possible variance is inversely proportional to averaging time, like in time-frequency metrology, and not limited by the instrumental resolution. Our measurement results show that in this case sample standard deviation of the mean (0.00003 mg) can be more than ten times smaller than that of a single result (0.0005 mg) or the Type B component of the digital resolution (0.00029 mg).

Resolution upgrade toward 6-bit optical quantization using power-to-wavelength conversion for photonic analog-to-digital conversion

We demonstrate a resolution upgrade toward 6 bit optical quantization using a power-to-wavelength conversion without an increment of system parallelism. Expansion of a full-scale input range is employed in conjunction with reduction of a quantization step size with keeping a sampling-rate transparency characteristic over several 100 sGS/s. The effective number of bits is estimated to 5.74 bit, and the integral nonlinearity error and differential nonlinearity error are estimated to less than 1 least significant bit.

Multiple Description Coding With Randomly and Uniformly Offset Quantizers

In this paper, two multiple description coding schemes are developed, based on prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers, respectively. In both schemes, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. In the first method, due to predictive coding, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped. The optimal reconstruction is obtained by finding the intersection of all received bins. In the second method, joint dequantization is also used, but near-uniform offsets are created among different low-rate quantizers by quantizing the predictions and by employing unequal deadzones. By generalizing the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained for the first method, and a lower bound is obtained for the second method. The schemes are then applied to lapped transform-based multiple description image coding. The closed-form expressions enable the optimization of the lapped transform. An iterative algorithm is also developed to facilitate the optimization. Theoretical analyzes and image coding results show that both schemes achieve better performance than other methods in this category.

Proxy-Based Multi-Stream Scalable Video Adaptation Over Wireless Networks Using Subjective Quality and Rate Models

Despite growing maturity in broadband mobile networks, wireless video streaming remains a challenging task, especially in highly dynamic environments. Rapidly changing wireless link qualities, highly variable round trip delays, and unpredictable traffic contention patterns often hamper the performance of conventional end-to-end rate adaptation techniques such as TCP-friendly rate control (TFRC). Furthermore, existing approaches tend to treat all flows leaving the network edge equally, without accounting for heterogeneity in the underlying wireless link qualities or the different rate utilities of the video streams. In this paper, we present a proxy-based solution for adapting the scalable video streams at the edge of a wireless network, which can respond quickly to highly dynamic wireless links. Our design adopts the recently standardized scalable video coding (SVC) technique for lightweight rate adaptation at the edge. Leveraging previously developed rate and quality models of scalable video with both temporal and amplitude scalability, we derive the rate-quality model that relates the maximum quality under a given rate by choosing the optimal frame rate and quantization stepsize. The proxy iteratively allocates rates of different video streams to maximize a weighted sum of video qualities associated with different streams, based on the periodically observed link throughputs and the sending buffer status. The temporal and amplitude layers included in each video are determined to optimize the quality while satisfying the rate assignment. Simulation studies show that our scheme consistently outperforms TFRC in terms of agility to track link qualities and overall subjective quality of all streams. In addition, the proposed scheme supports differential services for different streams, and competes fairly with TCP flows.

강한 광대역정규잡음 환경에서 GPS 상용 수신기 양자화기의 변환 손실 분석

This paper has derived the conversion loss according to the synchronized condition between the transmitted and locally generated spreading signals for the civil global positioning system (GPS) receiver in the heavy wideband Gaussian noise environments. From this, the outputs of the 2-bit nonuniform quantizer, which has the minimum conversion loss, is set to and , while the quantization step size is approximated to the jamming-to-signal power ratio.

2-Step Scalar Deadzone Quantization for Bitplane Image Coding

Modern lossy image coding systems generate a quality progressive codestream that, truncated at increasing rates, produces an image with decreasing distortion. Quality progressivity is commonly provided by an embedded quantizer that employs uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy. This paper introduces a 2-step scalar deadzone quantization (2SDQ) scheme that achieves same coding performance as that of USDQ while reducing the coding passes and the emitted symbols of the bitplane coding engine. This serves to reduce the computational costs of the codec and/or to code high dynamic range images. The main insights behind 2SDQ are the use of two quantization step sizes that approximate wavelet coefficients with more or less precision depending on their density, and a rate-distortion optimization technique that adjusts the distortion decreases produced when coding 2SDQ indexes. The integration of 2SDQ in current codecs is straightforward. The applicability and efficiency of 2SDQ are demonstrated within the framework of JPEG2000.

Robust quantisation index modulation‐based approach for image watermarking

In this study, a robust image watermarking based on the quantisation index modulation (QIM) method is proposed. Conventional QIM methods employ a fixed quantisation step-size that results in poor robustness of the algorithm. Here, the quantisation step-size in the QIM method is adaptively selected using a power-law function and with the aid of the side information, the proposed method is invariant to gain and rotation attack. To keep the watermark imperceptible and increase its robustness, the low-frequency components of high-entropy image blocks are used for data hiding. The analytical error probability and embedding distortion are derived and assessed by simulations on artificial signals. The optimum parameter in the power-law function is obtained based on minimising the error probability. Experimental results confirm the superiority of the proposed technique against common attacks in comparison with the recently proposed methods.

Blind structural similarity estimation of digital images using quantized discrete cosine transform coefficients

Objective image quality assessment is used to develop a quantitative measure in order to predict perceived image quality by exploiting a variety of known properties of the human visual system (HVS). A new paradigm for quality assessment of the image is based on the structural similarity (SSIM) index, which takes advantage of characteristics of the HVS. In order to estimate SSIM, we need to know the source image to quantify the visibility of errors between the distorted image and the referenced image. In many practical applications, however, the reference image is not available and a blind quality assessment should be utilized. In this paper, an algorithm for statistical estimation of the SSIM based on the probability density functions (pdfs) of quantized discrete cosine transform (DCT) coefficients is presented. In the proposed method, we assume that the pdfs of the original DCT coefficients follow a specific distribution. The parameters of this distribution are then obtained from the quantization step size and quantized DCT coefficients of the distorted image, which are then used to calculate the SSIM metric. Our proposed method is, therefore, applicable to the encoding schemes that involve DCT quantization such as JPEG encoding. Numerical results show that the proposed SSIM estimation method provides relative errors that are generally smaller than those of the available peak signal-to-noise ratio estimation schemes for DCT-based images.

Video Watermarking Using Spatio-Temporal Masking For ST-DM

Traditional Spread Transform Dither Modulation (ST-DM) is based on the fixed quantization step size, which does not exploit the features of the video contents. Spatio-temporal masking function represents the visual threshold under which human eyes are usually not sensitive. In this paper, we adopts not only the spatial contrast sensitivity function (CSF), the luminance masking function, the contrast masking function but also the spatio-temporal masking function to account for the model of human eyes to the video. we propose using spatio-temporal perceptual model to adaptively select the quantization step size based on the calculated masking function. Experimental results show that the proposed scheme guarantees high peak signal to noise ratio (PSNR). Furthermore, compared with the scheme with fixed step size, the proposed scheme is more robust against temporal frame averaging (TFA) attack.

Digital Image Watermarking Scheme Using Discrete Wavelet Transform Domain, Quantization, and Genetic Algorithm

Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. Watermarking is identified as a major means to achieve copyright protection. The algorithm proposed in this work, is to use a blind watermarking scheme based on the Discrete Wavelet Transform (DWT). Watermark components are embedded in the LL subband of the 4th DWT level of the host image by quantizing coefficients of the LL subband to improve the watermark robustness. The Genetic Algorithm (GA) is used for optimizing the quantization step size parameter, and the strength of factors. The host image used is a 512×512 gray scale image and the watermark image is a 32×32 binary logo. The proposed scheme was tested against mostly known threats and it proves to give good robustness. Also, it still gives a high quality watermarked image. MATLAB Program was used to perform the watermarking task.

Distributed LMS estimation over networks with quantised communications

This article investigates the problem of distributed least mean-square (D-LMS) estimation over a network with quantised communication. Each node in the network has a quantiser consisting of a first-order dynamical encoder-decoder and can only communicate with its neighbours. Then a new D-LMS estimator is proposed by employing a weighted sum of the internal state differences between each node's quantiser and those of its neighbours. Performance analysis of the proposed quantised D-LMS algorithm is studied in terms of mean-square transient and steady-state measurements. We show that for Gaussian data and sufficiently small step sizes, the proposed cooperative D-LMS with quantisation is mean-square stable in all quantisation levels including the 1-bit case, and its performance approaches the cooperative D-LMS without quantisation when the quantisation step size is fairly small. Furthermore, although in unquantised case (infinite precision), the cooperative D-LMS always outperforms the non-cooperative D-LMS scheme (without communications among the neighbours); however, we show that due to the existence of quantisation error, the cooperative D-LMS with quantisation does not always outperform the non-cooperative D-LMS scheme, especially when the quantisation step size is quite large. Finally, numerical simulations also demonstrate that our theoretical performance matches well with experimental performance.