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.
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.
We present error sensitivity metric for a macroblock's bit stream. This metric can be used in unequal error protection and optimal resync-marker insertion.
ISBN:
(纸本)0780374908
We present error sensitivity metric for a macroblock's bit stream. This metric can be used in unequal error protection and optimal resync-marker insertion.
In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. P...
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In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.
This report proposes a new lossless coding of images. The decoder can expand a rough image from a part of the bit stream (compressed data) and also it can expand the original image from the rest. This functionality is...
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This report proposes a new lossless coding of images. The decoder can expand a rough image from a part of the bit stream (compressed data) and also it can expand the original image from the rest. This functionality is useful for progressive transmission of images or browsing images in a huge database. The new method is based on a reversible wavelet (RWT) and a lossless multi-channel prediction (LMP). Filter coefficients of the LMP are optimized for each input image so that the total bit rate is minimized. Experimental results indicate its effectiveness for use of lossless image coding. Entropy rate is reduced by 0.35 bpp in average for some images.
In this paper, we propose a new perceptual quantization method for predictive coding of images, where the quantization step size is adaptively changed according to the perceptibility of the distortion incurred to the ...
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In this paper, we propose a new perceptual quantization method for predictive coding of images, where the quantization step size is adaptively changed according to the perceptibility of the distortion incurred to the current pixel. We measure the perceptibility using only the previously reconstructed samples which are also available at the decoder, so that any quantization-related overhead information does not need to be transmitted to the decoder. The proposed method can also provide the lossless concatenated coding property when applied to multigeneration of images since the same information on the perceptibility is available at the second time encoding. experiments show that the proposed perceptual quantization method provides much better subjective picture quality than the non-perceptual one when applied to the JPEG-LS standard.
We present a new speech coding, named neural predictive coding, based on an original neural predictive structure. The system is designed to predict the samples of a speech signal window from previous ones. The goal of...
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We present a new speech coding, named neural predictive coding, based on an original neural predictive structure. The system is designed to predict the samples of a speech signal window from previous ones. The goal of this coding is to extract the signal discriminative features relative to the database from which it is extracted. After a precise description of our coding, we discuss about the coding phase of the algorithm. We present experiences which show an improvement on telephone speech recognition compared with other predictive or frequency coding. The signal windows are extracted from the telephone speech signal database NTIMIT.
This paper presents an adaptive non-linear method for the predictive coding of images using multilayer perceptrons. By incorporating causal and localised training on the actual data being coded, rather than training s...
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This paper presents an adaptive non-linear method for the predictive coding of images using multilayer perceptrons. By incorporating causal and localised training on the actual data being coded, rather than training separate data, the network weights are continuously updated. This results in a highly adaptive predictor, with localised optimisation based on the stochastic gradient learning. The causal nature of the training means no transmission overhead is required and also enables lossless coding of the images. In addition to the adaptive prediction, the results presented here also incorporate an arithmetic coding scheme, producing results which are better than CALIC and comparable to TMW, the state of the art lossless compression in the literature. This shows that near-optimal results can be obtained with the fundamental concept of adaptive training. The use of a neural network provides a simple means for performing this training.
We propose an algorithm for erasure recovery in predictive coding schemes, where erasures can cause catastrophic error propagation. The recovery algorithm is based on sending multiple descriptions of the source and us...
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We propose an algorithm for erasure recovery in predictive coding schemes, where erasures can cause catastrophic error propagation. The recovery algorithm is based on sending multiple descriptions of the source and using a deterministic distance measure to find the most likely estimate for the lost data, given the received data and the side information. Results show that we can recover from short burst erasures and that for long bursts (more than 10% of the samples are lost) we can recover to within 0.4 dB of the original DPCM performance.
The main point made in this article is;that the representational capacity and inherent function of any neuron, neuronal population or cortical area is dynamic and! context-sensitive. This adaptive rand contextual spec...
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The main point made in this article is;that the representational capacity and inherent function of any neuron, neuronal population or cortical area is dynamic and! context-sensitive. This adaptive rand contextual specialisation is mediated by functional integration or interactions among brain systems with a special emphasis on backwards or top-down connections. The critical notion is that neuronal responses, in any given cortical area, can represent different things at different times, Our argument is developed under the perspective of generative models of functional brain architectures, where higher-level systems provide a prediction of the inputs to lower-level regions. Conflict between the two is resolved by changes in the higher-level representations, driven by the resulting error in lower regions, until the mismatch is 'cancelled', In this model the specialisation of any region is determined both by bottom-up driving inputs and by top-down predictions. Specialisation is therefore not an intrinsic property of any region but depends on both forward and backward connections with other areas. Because these other areas have access to the context in which the inputs are generated they are in a position to modulate the selectivity or specialisation of lower areas. The implications for 'classical' models (e,g., classical receptive fields in electrophysiology, classical specialisation in neuroimaging and connectionism in cognitive models) are severe and suggest these models provide incomplete accounts of real brain architectures, Generative models represent a far more plausible framework for understanding selective neurophysiological responses and how representations are constructed in the brain. (C) 2001 Elsevier Science Inc.
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