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IEEE International Conference on Image Processing

作者： D.W. Lin Department of Electronics Engineering and Center for Telecommunications Research National Chiao Tung University Hsinchu Taiwan

We point out that a prevalent form of fractal image coding can be viewed as a kind of generalized predictive coding. Several key issues in predictive coding are the prediction gain, the design of codebooks for predictors and prediction residuals, shaping of reconstruction errors, and codec complexity. Fractal coding can yield higher prediction gains than conventional predictive coding by its use of noncausal predictors and long-term predictors. However, noncausal prediction necessitates iterative decoding and long-term predictors require search over a large area, both of which increase codec complexity. Design of predictors and prediction codebooks for fractal coding has relied much on heuristics. Drawing on known results about predictive coding, we outline several directions for codec design, among which are short-term prediction and transform coding or vector quantization of prediction residuals. Shaping of reconstruction errors by noise-feedback or analysis-by-synthesis coding may also be beneficial.< >

关键词： Fractals Image coding predictive coding Codecs Image reconstruction Iterative decoding Vector quantization Distortion measurement Noise shaping Transform coding

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Application of predictive coding in the evolution of artificial neural network

Application of predictive coding in the evolution of artific...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： H. Mohabeer K.M.S Soyjaudah Department of Electrical and Electronic Engineering University of Mauritius Reduit Mauritius

In this paper we propose a new encoding scheme utilises predictive coding technique in order to increase the efficiency of evolving artificial neural network. The predictor encodes the sample data fed to the system and the artificial neural network acts as the decoder. The latter is trained using a data model created via predictive coding, which is generated from the initial sample. Only the residual data output from the encoder is fed to the artificial neural network for authentication. Distributed and local processing has been simultaneously used in parallel and in synchrony. Comparison of the simulation results with those obtained using traditional methods such as selective biometric features shows an improvement in efficiency of up to 80% while utilising a lower complexity neural network.

关键词： Artificial neural networks Encoding Genetic algorithms Biological cells predictive coding Biological neural networks Training

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Multiple component predictive coding framework of still images

Multiple component predictive coding framework of still imag...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiyuan Hu Weiping Xia Silong Peng Wen-Liang Hwang Institute of Automation Chinese Academy and Sciences Beijing China Institute of Information Science Academia Sinica Taipei Taiwan

In this paper, we propose a multiple component predictive coding framework. We firstly separate the reconstructed image into several subcomponents; and then predict each subcomponent independently but encode them together. To separate image into multiple subcomponents, we also propose a fast operator-based image separation algorithm. With the help of multicomponent prediction strategy, our prediction results can achieve superior performance than the H.264/AVC intra frame prediction method for images containing rich textures. By adopting the residue coding method used in H.264/AVC, we compare the compression efficacy of our proposed algorithm with the state-of-art JPEG2000 and H.264/AVC intra frame compression algorithms in the experimental part. The numerical results show that our algorithm is better than both H.264/AVC intra frame coding algorithm and JPEG2000 algorithm for images with ample textures.

关键词： Image coding Prediction algorithms Image reconstruction Transform coding Codecs predictive coding Image edge detection

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Speaker and Style Disentanglement of Speech Based on Contrastive predictive coding Supported Factorized Variational Autoencoder

Speaker and Style Disentanglement of Speech Based on Contras...

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European Signal Processing Conference (EUSIPCO)

作者： Yuying Xie Michael Kuhlmann Frederik Rautenberg Zheng-Hua Tan Reinhold Haeb-Umbach Department of Electronic Systems Aalborg University Denmark Department of Communications Engineering Paderborn University Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Speech signals encompass various information across multiple levels including content, speaker, and style. Disentanglement of these information, although challenging, is important for applications such as voice conversion. The contrastive predictive coding supported factorized variational autoencoder achieves unsupervised disentanglement of a speech signal into speaker and content embeddings by assuming speaker info to be temporally more stable than content-induced variations. However, this assumption may introduce other temporal stable information into the speaker embeddings, like environment or emotion, which we call style. In this work, we propose a method to further disentangle non-content features into distinct speaker and style features, notably by leveraging readily accessible and well-defined speaker labels without the necessity for style labels. Experimental results validate the proposed method's effectiveness on extracting disentangled features, thereby facilitating speaker, style, or combined speaker-style conversion.

关键词： Training Speech coding Europe predictive coding Signal processing Feature extraction Reverberation

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Nonlinear predictive coding for streaming video

Nonlinear predictive coding for streaming video

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IEEE Region 10 International Conference TENCON

作者： M. Vandana S. Jayaraman PSG College of Technology Coimbatore India

The coding of multimedia data streams is a vital factor in how well, and to what extent a given network can support popular applications. coding of speech and video, the two significant categories of data under consideration, currently follows the standards based on linear prediction, and MPEG respectively. The work documented in this paper is directed towards the development of an algorithm which permits coding of both voice and video data in an integrated manner with the same technique being used as the base for both the speech and the video coding algorithm of the system. The basic technique adopted for this integrated standard is M-ary predictive coding (MPC) [M. Vandana, January 2003]. MPC is a nonlinear model-based coding scheme which has currently been implemented successfully for speech coding [M. Vandana, January 2003]. The work documented in this paper involved the successful development and implementation of an MPC-based video coding algorithm.

关键词： predictive coding Streaming media Equations predictive models Frequency Speech coding Video coding Image coding Displays Discrete cosine transforms

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Highly efficient contour-based predictive shape coding

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PATTERN RECOGNITION LETTERS 2015年 65卷 131-136页

作者： Zhu, Zhong-jie Wang, Yu-er Jiang, Gang-yi Zhejiang Wanli Univ Ningbo Key Lab DSP Ningbo 315100 Zhejiang Peoples R China Ningbo Univ Inst Circuits & Syst Ningbo 315211 Zhejiang Peoples R China

A new scheme for contour based predictive shape coding is proposed aiming to acquire high coding efficiency, where the temporal correlations among object contours are effectively exploited. For a given binary shape image, the object contours are firstly extracted and thinned to be perfect single-pixel width followed by chain based representation. Then a chain based motion estimation and compensation technique is developed to remove temporal correlations among object contours to reduce the data to be encoded. Finally, by further exploiting the spatial correlations within chain links, a novel method is introduced to efficiently encode the residuals together with the motion displacements. Experiments are conducted and the results show that the proposed scheme is considerably more efficient than the existing techniques. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image coding Shape coding predictive coding High efficiency

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Lossless Information Hiding Scheme Based on predictive coding

Lossless Information Hiding Scheme Based on Predictive Codin...

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Tzu-Chuen Lu Cheng-Chun Huang Siang-Ru Liao Department of Information Management Chaoyang University of Technology Taichung Taiwan

In recent years, many lossless information hiding techniques have been proposed, such as difference expansion, integer transformation method, histogram modification and so on. Histogram modification modifies the maximum pixel value of the histogram which generated from a host image to embed secret messages. The image quality of this method is quite good but the information capacity is low. To solve this problem, we propose a lossless information hiding scheme in this paper. The proposed scheme uses predictive coding technique to generate an external histogram and an internal histogram. Both of them are used to conceal the secret message. According to the experimental results, the proposed method can not only increase the information capacity but also keep the image quality.

关键词： predictive coding Histograms Image segmentation Image coding Statistics Image generation Gray-scale Pixel Image quality Data mining

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Balancing Active Inference and Active Learning with Deep Variational predictive coding for EEG

Balancing Active Inference and Active Learning with Deep Var...

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IEEE International Conference on Systems, Man and Cybernetics

作者： André Ofner Sebastian Stober Otto von Guericke University Magdeburg Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

This paper discusses representation learning from electroencephalographic (EEG) signal with deep variational predictive coding networks. We introduce a hierarchical probabilistic network that minimises prediction error at multiple levels of spatio-temporal abstraction. While the lowest layer predicts brain activity directly, higher layers abstract away from the data and predict sequences of the hidden states in lower layers. The network captures both expected and actual uncertainty by relating predicted state posteriors. Each layer minimises (expected) surprise either with or without sampling new evidence from the layer below. This structure motivates both active learning and active inference as means to learn representations. Active learning refers to model parameter exploration which allows to learn regularities, especially when they are stable between trials. Active inference refers to hidden state exploration, a process that enables dynamic inference of the current context using the learned generative model. We train the model on EEG data recorded during free reading and evaluate adaptive EEG prediction in the context of Fixation Related Potentials (FRPs).

关键词： Adaptation models Uncertainty predictive coding Brain modeling Probabilistic logic Electroencephalography Context modeling

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Self-supervised Underwater Source Localization based on Contrastive predictive coding

Self-supervised Underwater Source Localization based on Cont...

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IEEE SENSORS

作者： Xiaoyu Zhu Hefeng Dong Pierluigi Salvo Rossi Martin Landrø Norwegian University of Science and Technology Trondheim Norway

This work introduces a two-step self-supervised learning scheme, namely contrastive predictive coding (CPC), for underwater source localization. In the first step, a CPC-based self-supervised feature extractor is trained with the acoustic signals. In the second step, the encoder with frozen parameters is taken from the trained feature extractor and connected with a multi-layer perceptron (MLP) trained for source localization on a small labeled dataset. This approach is evaluated on a public dataset, SWellEx-96 Event S5, against an autoencoder (AE) scheme and a purely supervised scheme. The results indicate that the CPC scheme has the best performance and can extract the slow-changing features related to the source.

关键词： Location awareness Training predictive coding Sensor phenomena and characterization Feature extraction Acoustics Performance analysis

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Performance evaluation of hybrid coding of images using wavelet transform and predictive coding

Performance evaluation of hybrid coding of images using wave...

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International Conference on Computational Intelligence and Multimedia Applications

作者： S.R. Subramanya C. Sabharwal Intelligent Systems Center University of Missouri Rolla Rolla MO USA Department of Computer Science University of Missouri Rolla Rolla MO USA

ISBN: (纸本)0769513123

Image compression techniques are necessary for the storage of huge amounts of digital images using reasonable amounts of space, and for their transmission with limited bandwidth. Several techniques such as predictive coding, transform coding, subband coding, wavelet coding, and vector quantization have been used in image coding. While each technique has some advantages, most practical systems use hybrid techniques which incorporate more than one scheme. They combine the advantages of the individual schemes and enhance the coding effectiveness. This paper proposes and evaluates a hybrid coding scheme for images using wavelet transforms and predictive coding. The performance evaluation is done using a variety of different parameters such as kinds of wavelets, decomposition levels, types of quantizers, predictor coefficients, and quantization levels. The results of evaluation are presented.

关键词： Image coding Wavelet transforms predictive coding Frequency Signal resolution Transform coding Discrete wavelet transforms Vector quantization Signal processing Image storage

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