Simulation results are presented comparing the performances of four backward adaptive lattice algorithms for updating the short-term predictors in adaptive predictive coding (APC) and differential pulse code modulatio...
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Simulation results are presented comparing the performances of four backward adaptive lattice algorithms for updating the short-term predictors in adaptive predictive coding (APC) and differential pulse code modulation (DPCM) code generators for low-delay tree coding of speech at 16 and 9.6 kb/s. The algorithms studied are the least-squares lattice, the exponential window lattice, the signal-driven lattice, and the residual-driven lattice. Ideal channels are emphasized, and comparisons are based primarily upon frequency-weighted signal-to-noise ratio and subjective listening tests.< >
We study sequential transmission of Gauss-Markov sources over erasure channels under a zero decoding delay constraint. A two-stage coding scheme which can be described as a hybrid between predictive coding with limite...
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ISBN:
(纸本)9781467377058
We study sequential transmission of Gauss-Markov sources over erasure channels under a zero decoding delay constraint. A two-stage coding scheme which can be described as a hybrid between predictive coding with limited past and quantization & binning is proposed. This scheme can achieve significant performance gains over baseline schemes in simulations involving i.i.d. erasure channels, and in certain regimes can attain performance close to a fundamental lower bound. We consider an information theoretic model for streaming that explains the weakness of baseline schemes (e.g., predictive coding, memoryless binning, etc.) and illustrates the advantage of our proposed hybrid scheme over these. Techniques from multi-terminal source coding are used to derive a new lower bound on the compression rate and identify cases when the hybrid coding scheme is close to optimal. We discuss qualitatively the interplay between the parameters of our information theoretic model and the statistical models used in simulations.
This paper presents a method for distortion-optimized streaming of predictively coded video over packet networks with varying delay. In networks with significant delay variations, coded video frames can arrive late at...
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This paper presents a method for distortion-optimized streaming of predictively coded video over packet networks with varying delay. In networks with significant delay variations, coded video frames can arrive late at the decoder and miss their respective display deadlines. Furthermore, due to predictive coding, a late frame can also prevent a number of subsequent frames from being displayed properly, where the number of affected frames or degree of distortion depends on the particular coding dependencies of the late frame. In this paper, we present an optimized video streaming strategy based on frame reordering for networks with significant delay variations. This streaming strategy minimizes distortion by exploiting the fact that different late frames result in different degrees of distortion. We model the router-induced delay in a wired network with an analytical PDF and we model the link-layer retransmission delay of a wireless network with the 3GPP specification for W-CDMA radio link control. We compute the distortion for different frame reorderings using the network delay models and a source model that accounts for the prediction dependencies of predictively coded video. Our optimized streaming strategies are shown to reduce the number of late frames by 14 to 23% for the situations examined.
The frequency selective extrapolation extends an image signal beyond a limited number of known samples. This problem arises in image and video communication in error prone environments where transmission errors may le...
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The frequency selective extrapolation extends an image signal beyond a limited number of known samples. This problem arises in image and video communication in error prone environments where transmission errors may lead to data losses. In order to estimate the lost image areas, the missing pixels are extrapolated from the available correctly received surrounding area which is approximated by a weighted linear combination of basis functions. In this contribution, we integrate the frequency selective extrapolation into the H.264/AVC coder as spatial concealment method. The decoder reference software uses spatial concealment only for I frames. Therefore, we investigate the performance of our concealment scheme for I frames and its impact on following P frames caused by error propagation due to predictive coding. Further, we compare the performance for coded video sequences in TV quality against the non-normative concealment feature of the decoder reference software. The investigations are done for slice patterns causing chequerboard and raster scan losses enabled by flexible macroblock ordering (FMO).
Image compression using stochastic artificial neural networks (SANNs) is studied. The ideal is to store an image in a stable distribution of a stochastic neural network. Given an input image f epsilon F, one can find ...
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Image compression using stochastic artificial neural networks (SANNs) is studied. The ideal is to store an image in a stable distribution of a stochastic neural network. Given an input image f epsilon F, one can find a SANN t epsilon T such that the equilibrium distribution of this SANN is the given image f. Therefore, the input image, f, is encoded into a specification of a SANN, t. This mapping from F (image space) to T (parameter space of SANN) defines the SANN transformation. It is shown that the compression ratio R of the SANN transformation is R=O(n/(K (log n)/sup 2/)) where n is the number of pixels. To complete a SANN transformation, SANN equations must be solved. Two SANN equations are presented. The solution of SANN is briefly discussed.< >
This paper presents the adaptation via evolutionary techniques of a pixel predictor for lossless image compression. The pixel prediction is based on a linear combination of some neighbor pixels. The evolutionary algor...
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This paper presents the adaptation via evolutionary techniques of a pixel predictor for lossless image compression. The pixel prediction is based on a linear combination of some neighbor pixels. The evolutionary algorithm selects the coefficients and the pixels involved in the pixel prediction. Experiments carried out on gray level images of the proposed system show a performance comparable and in some cases better than existing predictive coding techniques.
This paper presents an algorithm for segmentation of image sequences where specially aspects for object oriented coding are taken into account. A fundamental requirement of such applications is the temporal stability ...
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This paper presents an algorithm for segmentation of image sequences where specially aspects for object oriented coding are taken into account. A fundamental requirement of such applications is the temporal stability of the segmentation. This is improved compared to other existing approaches by including motion estimation into the segmentation process. Additionally, a hierarchical approach enables an efficient predictive coding on one hand and a semantic data access on the other hand. As a direct result from using full colour information for the segmentation process, coding of the chrominance information can be done with extremely high compression ratios. Thus there is nearly no extra coding effort for colour images compared to greyscale images.
We investigate several problems in scanning of multidimensional data arrays, such as universal scanning and prediction ("scandiction", for short), and scandiction of noisy data arrays. These problems arise i...
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ISBN:
(纸本)142440505X
We investigate several problems in scanning of multidimensional data arrays, such as universal scanning and prediction ("scandiction", for short), and scandiction of noisy data arrays. These problems arise in several aspects of image and video processing, such as predictive coding, filtering and denoising. In predictive coding of images, for example, an image is compressed by coding the prediction error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given image, what is the resulting minimum prediction loss, and if there exist specific scandiction schemes which are universal in some sense. More specifically, we investigate the following problems: first, given a random field, we examine whether there exists a scandiction scheme which is independent of the field's distribution, yet asymptotically achieves the same performance as if this distribution was known. This question is answered in the affirmative for the set of all spatially stationary random fields and under mild conditions on the loss function. We then discuss the scenario where a non-optimal scanning order is used, yet accompanied by an optimal predictor, and derive a bound on the excess loss compared to optimal scandiction. Finally, we examine the scenario where the random field is corrupted by noise, but the scanning and prediction (or filtering) scheme is judged with respect to the underlying noiseless field
Despite the rapid development of artificial intelligence technology in legal services around the world, little research work is being performed in the area of legal document classification in Korean language. In this ...
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Despite the rapid development of artificial intelligence technology in legal services around the world, little research work is being performed in the area of legal document classification in Korean language. In this paper, we propose and compare three different legal document classification approaches based on two deep neural network models, i.e., Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), and two word embedding schemes. Based on nearly 60,000 precedent case data, we obtained the highest classification accuracy (up to 86 percent) with the RNN model with Word2Vec embedding.
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