Multiple description (MD) coding has been shown to possess excellent error resilience capability for streaming video over lossy packet networks. Multiple description motion compensation coding (MDMC) also is a MD codi...
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ISBN:
(纸本)078038251X
Multiple description (MD) coding has been shown to possess excellent error resilience capability for streaming video over lossy packet networks. Multiple description motion compensation coding (MDMC) also is a MD coding with predictive coding scheme. In such a scheme there are "drift" error propagation, caused by the mismatch between the reference frames used in encoding and decoding. To control error propagation usually lose some predictive efficiency. In this paper, we propose an algorithm based on MD coding with modified multiple description scalar quantization (MDSQ) and leaky predictions to reduce drift without too much lowering of efficiency. Simulation results indicate that the proposed coder with leaky prediction outperforms the normal MDSQ coder even with same prediction scheme. The proposed coder is applicable for error prone wireless networks.
This paper proposes an error detection technique using fragile watermarking. The fragile watermark is embedded in the least significant bits of the selected transform coefficients decided to balance between deteriorat...
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This paper proposes an error detection technique using fragile watermarking. The fragile watermark is embedded in the least significant bits of the selected transform coefficients decided to balance between deterioration of PSNR value and error detection efficiency. The proposed method is usable without additional bits in the video bitstream and can be implemented very efficiently. This method will be useful in an error prone environment like a wireless channel.
In this paper, we propose an algorithm, which allows for embedded coding in L/sub /spl infin// sense, i.e., progressive near-lossless as well as lossless image compression. The method is based on a lossy plus near-los...
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In this paper, we propose an algorithm, which allows for embedded coding in L/sub /spl infin// sense, i.e., progressive near-lossless as well as lossless image compression. The method is based on a lossy plus near-lossless refinement layered compression scheme. As a basis for the lossy layer we use the JPEG2000 standard. We show that this approach allows for a better image quality and compression performance for large tolerance values than algorithms based on predictive coding. The compression performance of the algorithm in the lossless mode is about the same as that of the JPEG2000 standard. Another advantage of this technique is that it allows for all the benefits and functionality of the lossy compression algorithm (JPEG2000) at low bit rates.
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.
Digitized images contain huge amounts of information which strain, or exceed, the capacity for their real-time processing, storage, and retrieval. Various compression techniques have been developed to reduce the amoun...
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Digitized images contain huge amounts of information which strain, or exceed, the capacity for their real-time processing, storage, and retrieval. Various compression techniques have been developed to reduce the amount of data necessary for representation. The authors report on a hybrid image data compression procedure based on a multiple bases representation. The multiple bases representation technique described utilizes advantages of transform coding, vector quantization, and predictive coding, while aiming to circumvent the associated disadvantages of each. Preliminary results indicate that this procedure can outperform conventional compression methods, and yield high compression ratios while avoiding prohibitive computational complexity.< >
A method for amplitude modulated sinusoidal audio coding is presented that has low complexity and low delay. This is based on a sub-band processing system, where, in each subband, the signal is modeled as an amplitude...
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A method for amplitude modulated sinusoidal audio coding is presented that has low complexity and low delay. This is based on a sub-band processing system, where, in each subband, the signal is modeled as an amplitude modulated sum of sinusoids. The envelopes are estimated using frequency-domain linear prediction and the prediction coefficients are quantized. As a proof of concept, we evaluate different configurations in a subjective listening test, and this shows that the proposed method offers significant improvements in sinusoidal coding. Furthermore, the properties of the frequency-domain linear prediction-based envelope estimator are analyzed
Flickering is one of the most annoying temporal artifacts in predictive video coding. It will frequently appear in particular within slow moving low textured objects when the encoder is forced to skip macroblocks in o...
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Flickering is one of the most annoying temporal artifacts in predictive video coding. It will frequently appear in particular within slow moving low textured objects when the encoder is forced to skip macroblocks in order to save data rate. A very simple no-reference metric is derived for automatic detection of temporal flickering artifacts resulting from such macroblock skipping. Tests are performed for different H.264 encoded video sequences.
The authors consider the problem of optimal bit allocation in various forms of predictive coding, where the predictor itself has errors resulting from previous quantization. The solution to this problem has potential ...
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The authors consider the problem of optimal bit allocation in various forms of predictive coding, where the predictor itself has errors resulting from previous quantization. The solution to this problem has potential application to many forms of image and video coding where predictive coding is used. In predictive coding, the input to the quantizer can be decomposed into the innovation, i.e., the part of the quantizer input signal due to the quantization of the predictor. The natural question that arises is whether it is better to allocate more bits to the predictor, since quantization errors persist longer, or to allocate more bits to coding the total residual. This problem is analyzed for predictive video coding through the use of a simple parametric distortion-rate model for the propagation of quantization errors. This model provides a framework in which the optimal bit allocation problem can be solved in the presence of quantizer feedback. An exact MMSE (minimum mean-square error) solution is obtained that involves solving one nonlinear monotonic equation for one Lagrange multiplier, after which the bit allocation has a closed-form analytic solution. Since the MMSE solution does not produce equal distortion in all frames, the optimal MINMAX (minimize the maximum) bit allocation that minimizes the frame distortion subject to equal distortions per frame is also introduced.< >
predictive encoding with respect to multiple possible predictors is a common scenario encountered in many digital set-top box applications, such as redundant storage of video/audio data, real-time robust communication...
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predictive encoding with respect to multiple possible predictors is a common scenario encountered in many digital set-top box applications, such as redundant storage of video/audio data, real-time robust communication with peripherals and Internet video/audio telephony. A key problem associated with this scenario is that of predictive mismatch or drift. In the present paper, we pose the problem of predictive encoding with multiple possible predictors as a variant of the well-known Wyner-Ziv side-information problem. We propose an approach based on the use of coset codes for predictive encoding, for mitigating the effect of drift without overly sacrificing compression efficiency. The proposed approach can be used to improve coding performance in a wide range of practical applications such as multiple description coding, scalable coding and redundant storage of video/audio streams. We illustrate the efficacy of the proposed approach through a simple example based on the application of low-delay Internet telephony. Our results indicate that the proposed approach significantly outperforms conventional predictive encoding for communication over lossy channels.
The purpose of this research was to investigate the use of two-dimensional predictive coding of area functions for data rate reduction in LPC-based speech coding systems. predictive coding in the area function domain ...
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The purpose of this research was to investigate the use of two-dimensional predictive coding of area functions for data rate reduction in LPC-based speech coding systems. predictive coding in the area function domain is a technique for further data rate reduction on the LPC parameters. Two objective speech distortion measures were used for the optimization of predictor parameters and for objectively assessing the speech quality. The superiority of the LAR domain for coding was verified. The use of two-dimensional predictive coding was shown to be superior to the earlier one-dimensional systems under the conditions tested. Two-dimensional interpolation using a minimum mean square error (MMSE) criterion for interpolation coefficients was shown to be less effective than standard linear interpolation under the conditions tested. However, a one-dimensional nonlinear interpolation using MMSE criterion coefficients was shown comparable to standard linear interpolation.
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