By exploiting the commonly observed Laplacian probability distribution of audio, image, and video prediction residuals, many researchers proposed low complexity prefix codes to compress integer residual data. All thes...
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ISBN:
(纸本)9783540694212
By exploiting the commonly observed Laplacian probability distribution of audio, image, and video prediction residuals, many researchers proposed low complexity prefix codes to compress integer residual data. All these techniques treated predictions as integers despite being drawn from the real domain in lossless compression. Among these, Golomb coding is widely used for being optimal with non-negative integers that follow geometric distribution, a two-sided extension of which is the discrete analogue of Laplacian distribution. This paper for the first time presents a novel predictive codec which treats real-domain predictions without rounding to the nearest integers and thus avoids any coding loss due to rounding. The proposed codec innovatively uses the concept of distributed source coding by replacing the reminder part of Golomb code with the index of the coset containing the actual value.
This work presents a novel histogram-based reversible data hiding scheme. Although common histogram-based reversible data hiding schemes can achieve high image quality, embedding capacity is restricted because general...
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This work presents a novel histogram-based reversible data hiding scheme. Although common histogram-based reversible data hiding schemes can achieve high image quality, embedding capacity is restricted because general images usually do not contain a great number of pixels with the same pixel values. To improve embedding capacity and retain low distortion, the proposed scheme uses prediction-error values, which are derived from the difference between an original image and a predictive image, instead of using the original pixels to convey a secret message. In the proposed scheme, a predictive image is generated using the mean interpolation prediction method. Since the obtained predictive image is very similar to the original image, prediction-error values are to be tended to zero. That is, a great quantity of peak points gathers around zero. The proposed scheme takes full advantage of this property to increase embedding capacity and retain slight distortion. Moreover, a threshold is used to balance the tradeoff between embedding capacity and image quality, i.e. embedding capacity in the proposed scheme is scalable. Furthermore, only a threshold is needed to record, not a large amount of information of peak and zero points, when high embedding capacity is required. Additionally, a multilevel mechanism is employed to further increase embedding capacity. Experimental results indicate that the proposed scheme is superior to other reversible schemes in terms of both image quality and embedding capacity.
Through analyzing the principle of predictive coding, a steganalysis method based on predictive coding is proposed. The predictive coding is based on the relativity between the neighboring pixels. On the contrary, ste...
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Through analyzing the principle of predictive coding, a steganalysis method based on predictive coding is proposed. The predictive coding is based on the relativity between the neighboring pixels. On the contrary, steganograph will destroy the relativity between the neighboring pixels. Through computing and analysing the predictive error of cover image and steg image, it is obtained that steganograph will increase the predictive error. So the predictive error can be used to detect steganograph. The experiment shows that this method has a better ability of steganalysis.
We develop practical rate distortion bounds for speech coding based on composite source models and the PESQ-MOS distortion measure. Specifically, the bounds and formulated using composite source models for speech, the...
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We develop practical rate distortion bounds for speech coding based on composite source models and the PESQ-MOS distortion measure. Specifically, the bounds and formulated using composite source models for speech, the rate distortion function for Gaussian autoregressive sources, the classical reverse water-filling result, and conditional rate distortion theory, along with a recently devised MSE-to-PESQ_MOS mapping. The resulting rate distortion bounds are shown to lower bound the performance of the AMR, G.729, and G.718 standardized codecs, and based on the tightness of these bounds, to indicate how the performance of voice codecs might be improved.
Energy efficiency is critical in the design and deployment of wireless sensor networks. Data compression is a significant approach to reducing energy consumption of data gathering in multi-hop sensor networks. Existin...
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Energy efficiency is critical in the design and deployment of wireless sensor networks. Data compression is a significant approach to reducing energy consumption of data gathering in multi-hop sensor networks. Existing compression algorithms, however, only apply to either lossless or lossy compression, but not to both. This paper presents a unified algorithmic framework to both lossless and lossy data compression, thus effectively supporting the desirable flexibility of choosing either lossless or lossy compression in an on-demand fashion based on given applications. We analytically prove that the performance of the proposed framework for lossless compression is superior to or at least equivalent to that of traditional predictive coding schemes regardless of any entropy encoders used. We demonstrate the merits of our proposed framework in comparison with other recently proposed compression algorithms for wireless sensor networks including LEC, S-LZW and LTC using various real-world sensor data sets.
Distributed coding of correlated sources with memory poses a number of considerable challenges that threaten practical applications, particularly (but not only) in the context of sensor networks. This problem is stron...
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Distributed coding of correlated sources with memory poses a number of considerable challenges that threaten practical applications, particularly (but not only) in the context of sensor networks. This problem is strongly motivated by the obvious observation that most common sources exhibit temporal correlations that may be at least as important as spatial or inter-source correlations. This paper presents an analysis of the underlying tradeoffs, paradigms for coding systems, and approaches for distributed predictive coder design optimization. Motivated by practical limitations on both complexity and delay (especially for dense sensor networks) the focus here is on predictive coding. From the source coding perspective, the most basic tradeoff (and difficulty) is due to conflicts that arise between distributed coding and prediction, wherein 'standard' distributed quantization of the prediction errors, if coupled with imposition of zero decoder drift, would drastically compromise the predictor performance and hence the ability to exploit temporal correlations. Another challenge arises from instabilities in the design of closed loop predictors, whose impact has been observed in the past, but is greatly exacerbated in the case of distributed coding. The main contribution focuses on the tradeoffs encountered within a more general paradigm where decoder drift is allowable or unavoidable, and must be effectively accounted for and controlled. We briefly review our earlier results on which we build to derive an overall design optimization method that avoids the pitfalls of naive distributed predictive quantization and produces an optimized low complexity and low delay coding system.
We present a layered predictive compression approach for time-consistent dynamic 3D meshes. The algorithm decomposes each frame of a dynamic 3D mesh in layers employing patch-based mesh simplification techniques. This...
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We present a layered predictive compression approach for time-consistent dynamic 3D meshes. The algorithm decomposes each frame of a dynamic 3D mesh in layers employing patch-based mesh simplification techniques. This layered decomposition is consistent in time. Following the predictive coding paradigm, local temporal and spatial dependencies between layers and frames are exploited for compression. Prediction is performed vertex-wise from coarse to fine layers exploiting local linear and non-linear dependencies between vertex locations for compression. It is shown that a non-linear predictive exploitation of the proposed layered configuration of vertices can improve the compression performance upon other state-of-the-art approaches by more than 15% in domains relevant for applications.
A new hybrid predictive coding scheme for lossy data compression is introduced and studied here. The proposed technique attempts to improve the compression performance of a conventional adaptive predictive coder throu...
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A new hybrid predictive coding scheme for lossy data compression is introduced and studied here. The proposed technique attempts to improve the compression performance of a conventional adaptive predictive coder through three levels of prediction. The first level estimates and removes a time-varying mean signal. The second level uses DFT and IDFT to estimate and remove the predictable high frequency subband component of the signal, whereas the third level performs the adaptive predictive coding of the smoothed signal. The resultant residual is then quantized using an adaptive quantizer with 3 and 5 levels. Experimental results based on a group of test signals seem to indicate that the proposed hybrid scheme can improve the lossy compression achievable using a conventional adaptive predictive coder.
We present a linear predictive compression approach for time-consistent 3D mesh sequences supporting and exploiting scalability. The algorithm decomposes each frame of a mesh sequence in layers employing patch-based m...
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We present a linear predictive compression approach for time-consistent 3D mesh sequences supporting and exploiting scalability. The algorithm decomposes each frame of a mesh sequence in layers employing patch-based mesh simplification techniques. This layered decomposition is consistent in time. Following the predictive coding paradigm, local temporal and spatial dependencies between layers and frames are exploited for compression. Prediction is performed vertex-wise from coarse to fine layers exploiting the motion of already encoded 1-ring neighbor vertices for prediction of the current vertex location. It is shown that a predictive exploitation of the proposed layered configuration of vertices can improve the compression performance upon other state-of-the-art approaches by up to 16% in domains relevant for applications.
This paper investigates distributed predictivecoding of correlated sources with memory, which are communicated to a central receiver. This is the setting typically encountered in sensor networks. While source memor...
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ISBN:
(纸本)9781424413973;1424413974
This paper investigates distributed predictivecoding of correlated sources with memory, which are communicated to a central receiver. This is the setting typically encountered in sensor networks. While source memory may be exploited by distributed coding of large source blocks (vectors), the growth in complexity (and delay) is often unacceptable in practice, hence the interest in a low complexity predictive approach. We first consider the inherent "conflict" between distributed and predictive coding due to the impact of distributed quantization on the prediction loop. This is coupled with the effects of closed loop prediction, which destabilize standard Lloyd-like code design methods. An iterative algorithm is derived, which optimizes the overall system while imposing zero decoder drift due to distributed quantization. The approach circumvents convergence and stability issues of traditional predictive quantizer design by employing an "asymptotic closed loop" framework which is adapted for distributed predictive system design. The scheme efficiently utilizes both the temporal and inter-source correlations and subsumes as extreme special cases both separate source predictive coding, and distributed coding of memoryless correlated sources.
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